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Loet Leydesdorff - One of the best experts on this subject based on the ideXlab platform.
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The Interaction of 'Supply', 'Demand', and 'Technology' in Terms of Medical Subject Headings: A Triple Helix Model of Medical Innovations
SSRN Electronic Journal, 2020Co-Authors: Alexander M Petersen, Daniele Rotolo, Loet LeydesdorffAbstract:We develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on Triple Helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the Medical context, where uncertainty poses significant challenges to the governance of innovation. The Medical Subject Headings (MeSH) of MEDLINE/PubMed provide us with publication records classified within the categories “Diseases” (C), “Drugs and Chemicals” (D), “Analytic, Diagnostic, and Therapeutic Techniques and Equipment” (E) as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of Medical research areas are used as representative ‘entry perspectives’ of the Medical innovation process. These are: (i) Human Papilloma Virus, (ii) RNA interference, and (iii) Magnetic Resonance Imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that points to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with Medical innovation. Among the pairwise configurations in these contexts, the demand-technological capabilities (C-E) provided the strongest link, followed by the supply-demand (D-C) and the supply-technological capabilities (D-E) channels
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cited references and Medical Subject Headings mesh as two different knowledge representations clustering and mappings at the paper level
arXiv: Digital Libraries, 2016Co-Authors: Loet Leydesdorff, Jordan A Comins, Aaron A Sorensen, Lutz Bornmann, Iina HellstenAbstract:For the bioMedical sciences, the Medical Subject Headings (MeSH) make available a rich feature which cannot currently be merged properly with widely used citing/cited data. Here, we provide methods and routines that make MeSH terms amenable to broader usage in the study of science indicators: using Web-of-Science (WoS) data, one can generate the matrix of citing versus cited documents; using PubMed/MEDLINE data, a matrix of the citing documents versus MeSH terms can be generated analogously. The two matrices can also be reorganized into a 2-mode matrix of MeSH terms versus cited references. Using the abbreviated journal names in the references, one can, for example, address the question whether MeSH terms can be used as an alternative to WoS Subject Categories for the purpose of normalizing citation data. We explore the applicability of the routines in the case of a research program about the amyloid cascade hypothesis in Alzheimer's disease (AD). One conclusion is that referenced journals provide archival structures, whereas MeSH terms indicate mainly variation (including novelty) at the research front. Furthermore, we explore the option of using the citing/cited matrix for main-path analysis as a by-product of the software.
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a triple helix model of Medical innovation supply demand and technological capabilities in terms of Medical Subject Headings
Research Policy, 2016Co-Authors: Alexander M Petersen, Daniele Rotolo, Loet LeydesdorffAbstract:We develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on triple helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the Medical context, where uncertainty poses significant challenges to the governance of innovation. We use the Medical Subject Headings (MeSH) of MEDLINE/PubMed to identify publications within the categories “Diseases” (C), “Drugs and Chemicals” (D), “Analytic, Diagnostic, and Therapeutic Techniques and Equipment” (E) and use these as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of Medical research areas are used as representative ‘entry perspectives’ of the Medical innovation process. These are: (i) human papilloma virus, (ii) RNA interference, and (iii) magnetic resonance imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that point to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with Medical innovation. Among the pairwise configurations in these contexts, the demand–technological capabilities (C-E) provided the strongest link, followed by the supply–demand (D-C) and the supply–technological capabilities (D-E) channels.
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the interaction of supply demand and technological capabilities in terms of Medical Subject Headings a triple helix model of Medical innovation
arXiv: Digital Libraries, 2015Co-Authors: Alexander M Petersen, Daniele Rotolo, Loet LeydesdorffAbstract:We develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on Triple Helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the Medical context, where uncertainty poses significant challenges to the governance of innovation. The Medical Subject Headings (MeSH) of MEDLINE/PubMed provide us with publication records classified within the categories “Diseases” (C), “Drugs and Chemicals” (D), “Analytic, Diagnostic, and Therapeutic Techniques and Equipment” (E) as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of Medical research areas are used as representative ‘entry perspectives’ of the Medical innovation process. These are: (i) Human Papilloma Virus, (ii) RNA interference, and (iii) Magnetic Resonance Imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that points to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with Medical innovation. Among the pairwise configurations in these contexts, the demand-technological capabilities (C-E) provided the strongest link, followed by the supply-demand (D-C) and the supply-technological capabilities (D-E) channels.
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citation analysis with Medical Subject Headings mesh using the web of knowledge a new routine
Journal of the Association for Information Science and Technology, 2013Co-Authors: Loet Leydesdorff, Tobias OpthofAbstract:Citation analysis of documents retrieved from the Medline database (at the Web of Knowledge) has been possible only on a case-by-case basis. A technique is presented here for citation analysis in batch mode using both Medical Subject Headings (MeSH) at the Web of Knowledge and the Science Citation Index at the Web of Science (WoS). This freeware routine is applied to the case of "Brugada Syndrome," a specific disease and field of research (since 1992). The journals containing these publications, for example, are attributed to WoS categories other than "cardiac and cardiovascular systems", perhaps because of the possibility of genetic testing for this syndrome in the clinic. With this routine, all the instruments available for citation analysis can now be used on the basis of MeSH terms. Other options for crossing between Medline, WoS, and Scopus are also reviewed.
Patrick Ruch - One of the best experts on this subject based on the ideXlab platform.
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full texts representations with Medical Subject Headings and co citations network reranking strategies for trec 2014 clinical decision support track
Text REtrieval Conference, 2014Co-Authors: Julien Gobeill, Arnaud Gaudinat, Emilie Pasche, Patrick RuchAbstract:Abstract : In TREC 2014 Clinical Decision Support Track, the task was to retrieve full-texts relevant for answering generic clinical questions about Medical records. For this purpose, we investigated a large range of strategies in the five runs we officially submitted. Concerning Information Retrieval (IR), we tested two different indexing levels: documents or sections. Section indexing was clearly below (-40% in R-Precision). In the domain of Information Extraction, we enriched documents with Medical Subject Headings concepts that were collected from MEDLINE or extracted in the text with exact match strategies. We also investigated a target-specific semantic enrichment: MeSH terms representing diagnosis, treatments or tests (relying on UMLS semantic types) were used both in collection and in queries to guide the retrieval. Unfortunately, the MeSH representation was not as complementary with the text as we expected, and the results were disappointing. Concerning post-processing strategies, we tested the boosting of specific articles types (e.g. review articles, case reports), but the IR process already tended to favour these article types. Finally, we applied a reranking strategy relying on the cocitations network, thanks to normalized references provided in the corpus. This last strategy led to a slight improvement (+5%).
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TREC - Full-texts representations with Medical Subject Headings, and co-citations network reranking strategies for TREC 2014 clinical decision support track
2014Co-Authors: Julien Gobeill, Arnaud Gaudinat, Emilie Pasche, Patrick RuchAbstract:Abstract : In TREC 2014 Clinical Decision Support Track, the task was to retrieve full-texts relevant for answering generic clinical questions about Medical records. For this purpose, we investigated a large range of strategies in the five runs we officially submitted. Concerning Information Retrieval (IR), we tested two different indexing levels: documents or sections. Section indexing was clearly below (-40% in R-Precision). In the domain of Information Extraction, we enriched documents with Medical Subject Headings concepts that were collected from MEDLINE or extracted in the text with exact match strategies. We also investigated a target-specific semantic enrichment: MeSH terms representing diagnosis, treatments or tests (relying on UMLS semantic types) were used both in collection and in queries to guide the retrieval. Unfortunately, the MeSH representation was not as complementary with the text as we expected, and the results were disappointing. Concerning post-processing strategies, we tested the boosting of specific articles types (e.g. review articles, case reports), but the IR process already tended to favour these article types. Finally, we applied a reranking strategy relying on the cocitations network, thanks to normalized references provided in the corpus. This last strategy led to a slight improvement (+5%).
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query and document expansion with Medical Subject Headings terms at Medical imageclef 2008
Cross-Language Evaluation Forum, 2008Co-Authors: Julien Gobeill, Patrick Ruch, Xin ZhouAbstract:In this paper, we report on query and document expansion using Medical Subject Headings (MeSH) terms designed for Medical ImageCLEF 2008. In this collection, MeSH terms describing an image could be obtained in two different ways: either being collected with the associated MEDLINE's paper, or being extracted from the associated caption. We compared document expansion using both. From a baseline of 0.136 for Mean Average Precision (MAP), we reached a MAP of respectively 0.176 (+29%) with the first method, and 0.154 (+13%) with the second. In-depth analyses show how both strategies were beneficial, as they covered different aspects of the image. Finally, we combined them in order to produce a significantly better run (0.254 MAP, +86%). Combining the MeSH terms using both methods gives hence a better representation of the images, in order to perform document expansion.
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CLEF - Query and document expansion with Medical Subject Headings terms at Medical Imageclef 2008
Lecture Notes in Computer Science, 2008Co-Authors: Julien Gobeill, Patrick Ruch, Xin ZhouAbstract:In this paper, we report on query and document expansion using Medical Subject Headings (MeSH) terms designed for Medical ImageCLEF 2008. In this collection, MeSH terms describing an image could be obtained in two different ways: either being collected with the associated MEDLINE's paper, or being extracted from the associated caption. We compared document expansion using both. From a baseline of 0.136 for Mean Average Precision (MAP), we reached a MAP of respectively 0.176 (+29%) with the first method, and 0.154 (+13%) with the second. In-depth analyses show how both strategies were beneficial, as they covered different aspects of the image. Finally, we combined them in order to produce a significantly better run (0.254 MAP, +86%). Combining the MeSH terms using both methods gives hence a better representation of the images, in order to perform document expansion.
Fernando Fernandezllimos - One of the best experts on this subject based on the ideXlab platform.
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redefining the pharmacology and pharmacy Subject category in the journal citation reports using Medical Subject Headings mesh
International Journal of Clinical Pharmacy, 2017Co-Authors: Fernando Minguet, Teresa M Salgado, Claudio Santopadre, Fernando FernandezllimosAbstract:Background The Journal Citation Reports (JCR) Pharmacology and Pharmacy Subject category is heterogeneous. The inclusion of journals with basic and clinical scopes, which have different citation patterns, compromises comparability of impact factors among journals within the category. Objective To subdivide the Pharmacology and Pharmacy category into basic pharmacology, clinical pharmacology, and pharmacy based on the analyses of Medical Subject Headings (MeSH) as a proxy of journals’ scopes. Setting JCR. Method All articles, and respective MeSH, published in 2013, 2014, and 2015 in all journals included in the 2014 JCR Pharmacology and Pharmacy category were retrieved from PubMed. Several models using a combination of the 14 MeSH categories and specific MeSH tree branches were tested using hierarchical cluster analysis. Main outcome measure Distribution of journals across the subcategories of the JCR Pharmacology and Pharmacy Subject category. Results A total of 107,847 articles from 214 journals were included. Nine different models combining the MeSH categories M (Persons) and N (Health Care) with specific MeSH tree branches (selected ad-hoc) and Pharmacy-specific MeSH (identified in previous research) consistently grouped 142 journals (66.4%) in homogeneous groups reflecting their basic and clinical pharmacology, and pharmacy scopes. Ultimately, journals were clustered into: 150 in basic pharmacology, 43 in clinical pharmacology, 16 in basic pharmacology and clinical pharmacology, and 5 in pharmacy. Conclusion The reformulation of the Pharmacology and Pharmacy category into three categories was demonstrated by the consistent results obtained from testing nine different clustering models using the MeSH terms assigned to their articles.
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new pharmacy specific Medical Subject Headings included in the 2017 database
American Journal of Health-system Pharmacy, 2017Co-Authors: Fernando Fernandezllimos, Fernando Minguet, Teresa M SalgadoAbstract:The generation of a robust body of evidence starts with the identification of high-quality studies that can be subsequently synthesized in systematic reviews and meta-analyses. “Systematic reviews need systematic searchers” who use specific procedures and instruments to ensure the quality of
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quality of pharmacy specific Medical Subject Headings mesh assignment in pharmacy journals indexed in medline
Research in Social & Administrative Pharmacy, 2015Co-Authors: Fernando Minguet, Lucienne Van Den Boogerd, Teresa M Salgado, Fernando FernandezllimosAbstract:Abstract Background The Medical Subject Headings (MeSH) is the National Library of Medicine (NLM) controlled vocabulary for indexing articles. Inaccuracies in the MeSH thesaurus have been reported for several areas including pharmacy. Objectives To assess the quality of pharmacy-specific MeSH assignment to articles indexed in pharmacy journals. Methods The 10 journals containing the highest number of articles published in 2012 indexed under the MeSH ‘Pharmacists' were identified. All articles published over a 5-year period (2008–2012) in the 10 previously selected journals were retrieved from PubMed. MeSH terms used to index these articles were extracted and pharmacy-specific MeSH terms were identified. The frequency of use of pharmacy-specific MeSH terms was calculated across journals. Results A total of 6989 articles were retrieved from the 10 pharmacy journals, of which 328 (4.7%) were articles not fully indexed and therefore did not contain any MeSH terms assigned. Among the 6661 articles fully indexed, the mean number of MeSH terms was 10.1 (SD = 4.0), being 1.0 (SD = 1.3) considered as Major MeSH. Both values significantly varied across journals. The mean number of pharmacy-specific MeSH terms per article was 0.9 (SD = 1.2). A total of 3490 (52.4%) of the 6661 articles were indexed in pharmacy journals without a single pharmacy-specific MeSH. Of the total 67193 MeSH terms assigned to articles, on average 10.5% (SD = 13.9) were pharmacy-specific MeSH. A statistically significant different pattern of pharmacy-specific MeSH assignment was identified across journals (Kruskal–Wallis P Conclusions The quality of assignment of the existing pharmacy-specific MeSH terms to articles indexed in pharmacy journals can be improved to further enhance evidence gathering in pharmacy. Over half of the articles published in the top-10 journals publishing pharmacy literature were indexed without a single pharmacy-specific MeSH.
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characterization of the Medical Subject Headings thesaurus for pharmacy
American Journal of Health-system Pharmacy, 2014Co-Authors: Fernando Minguet, Lucienne Van Den Boogerd, Teresa M Salgado, Cassyano Januario Correr, Fernando FernandezllimosAbstract:Purpose The completeness and utility of pharmacy-oriented Medical Subject Headings (MeSH) relative to MeSH terminology pertaining to other healthcare professions (dentistry and nursing) are evaluated. Methods The 2013 version of the MeSH thesaurus—the standard vocabulary used by the National Library of Medicine (NLM) to index articles in PubMed and MEDLINE—was searched for dentistry-, nursing-, and pharmacy-specific terms using a truncation strategy (search terms: nurs*, dent*, and pharm* ); the hierarchical level of each term and the number of descendant terms (an indication of the granularity of the associated NLM-indexed content) were determined. PubMed searches were conducted to identify areas of the MeSH hierarchy containing dentistry- and nursing-specific terms but no equivalent pharmacy-specific term. Results The search of the MeSH thesaurus identified 145 terms representing dentistry-specific activities and 94 and 26 terms specific to nursing and pharmacy practice, respectively. Analysis of the three sets of MeSH terms indicated that dentistry-oriented MeSH terms were generally situated more prominently within the MeSH hierarchy than terms for nursing- and pharmacy-oriented research; the MeSH terminology oriented toward nursing or dentistry practice was relatively more granular, allowing for increased specificity and power of information retrieval during PubMed and MEDLINE searches. Seventeen proposed new MeSH terms describing key areas of pharmacy practice were identified; the inclusion of these terms in the MeSH hierarchy could substantially expand and improve the retrievability of NLM-indexed literature. Conclusion Imbalances and gaps were found in MeSH coverage of pharmacy concepts and terminology relative to MeSH terminology specific to the nursing and dentistry professions.
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identification of pharmacy specific Medical Subject Headings mesh lacking in the mesh thesaurus
Research in Social & Administrative Pharmacy, 2014Co-Authors: Fernando Fernandezllimos, Fernando Minguet, Lucienne Van Den Boogerd, Teresa M SalgadoAbstract:brand names, their compliance with the current pharmaceutical regulations and with the European Portuguese linguistic systems. Methods:Descriptive analytical study of a representative sample (N1⁄4474) of brands, selected from Portuguese National Medicines Formulary. Firstly, the linguistic evaluation was performed through a dedicated software comprising the quantification of words, letters and syllables in each brand, and including the identification of names with abbreviations and hyphens. Secondly, each brand was individually assessed in relation to their compliance with linguistic rules (e.g. letters or word segments that do not exist in Portuguese), as well as with pharmaceutical regulations (e.g. use of abbreviations). Results: Overall, 166 (35.1%) names were found not to be written in accordance with the orthographic Portuguese system. From the 308 compliant names, 124 (26.2%) included structures of very low frequency in current Portuguese (e.g. names finishing with “x”). The overall average number of letters by name was 8.7 ( 3.2), while 95.6% of the names were composed by less than 4 syllables, in line with general Portuguese. The overall average number of words by name was 1.3 ( 0.7) and there were 20 (4.2%) names presenting hyphens and 36 (7.6%) with abbreviations, which is noncompliant with pharmaceutical regulations. Additionally, 20 brands presented other regulatory issues, such as the use of words “rapid” or “hyper”. Conclusions: The high prevalence of names inspired by foreign languages, which are often deviant from the phonological and orthographic Portuguese systems, bring forward readability concerns. Challenging names suggest difficulties on medicines’ memorization and communication, particularly by low literate patients. Medicines’ brand names should not only follow marketing needs but also the purpose of being completely legible, improving medicines’ safe use.
Julien Gobeill - One of the best experts on this subject based on the ideXlab platform.
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full texts representations with Medical Subject Headings and co citations network reranking strategies for trec 2014 clinical decision support track
Text REtrieval Conference, 2014Co-Authors: Julien Gobeill, Arnaud Gaudinat, Emilie Pasche, Patrick RuchAbstract:Abstract : In TREC 2014 Clinical Decision Support Track, the task was to retrieve full-texts relevant for answering generic clinical questions about Medical records. For this purpose, we investigated a large range of strategies in the five runs we officially submitted. Concerning Information Retrieval (IR), we tested two different indexing levels: documents or sections. Section indexing was clearly below (-40% in R-Precision). In the domain of Information Extraction, we enriched documents with Medical Subject Headings concepts that were collected from MEDLINE or extracted in the text with exact match strategies. We also investigated a target-specific semantic enrichment: MeSH terms representing diagnosis, treatments or tests (relying on UMLS semantic types) were used both in collection and in queries to guide the retrieval. Unfortunately, the MeSH representation was not as complementary with the text as we expected, and the results were disappointing. Concerning post-processing strategies, we tested the boosting of specific articles types (e.g. review articles, case reports), but the IR process already tended to favour these article types. Finally, we applied a reranking strategy relying on the cocitations network, thanks to normalized references provided in the corpus. This last strategy led to a slight improvement (+5%).
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TREC - Full-texts representations with Medical Subject Headings, and co-citations network reranking strategies for TREC 2014 clinical decision support track
2014Co-Authors: Julien Gobeill, Arnaud Gaudinat, Emilie Pasche, Patrick RuchAbstract:Abstract : In TREC 2014 Clinical Decision Support Track, the task was to retrieve full-texts relevant for answering generic clinical questions about Medical records. For this purpose, we investigated a large range of strategies in the five runs we officially submitted. Concerning Information Retrieval (IR), we tested two different indexing levels: documents or sections. Section indexing was clearly below (-40% in R-Precision). In the domain of Information Extraction, we enriched documents with Medical Subject Headings concepts that were collected from MEDLINE or extracted in the text with exact match strategies. We also investigated a target-specific semantic enrichment: MeSH terms representing diagnosis, treatments or tests (relying on UMLS semantic types) were used both in collection and in queries to guide the retrieval. Unfortunately, the MeSH representation was not as complementary with the text as we expected, and the results were disappointing. Concerning post-processing strategies, we tested the boosting of specific articles types (e.g. review articles, case reports), but the IR process already tended to favour these article types. Finally, we applied a reranking strategy relying on the cocitations network, thanks to normalized references provided in the corpus. This last strategy led to a slight improvement (+5%).
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query and document expansion with Medical Subject Headings terms at Medical imageclef 2008
Cross-Language Evaluation Forum, 2008Co-Authors: Julien Gobeill, Patrick Ruch, Xin ZhouAbstract:In this paper, we report on query and document expansion using Medical Subject Headings (MeSH) terms designed for Medical ImageCLEF 2008. In this collection, MeSH terms describing an image could be obtained in two different ways: either being collected with the associated MEDLINE's paper, or being extracted from the associated caption. We compared document expansion using both. From a baseline of 0.136 for Mean Average Precision (MAP), we reached a MAP of respectively 0.176 (+29%) with the first method, and 0.154 (+13%) with the second. In-depth analyses show how both strategies were beneficial, as they covered different aspects of the image. Finally, we combined them in order to produce a significantly better run (0.254 MAP, +86%). Combining the MeSH terms using both methods gives hence a better representation of the images, in order to perform document expansion.
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CLEF - Query and document expansion with Medical Subject Headings terms at Medical Imageclef 2008
Lecture Notes in Computer Science, 2008Co-Authors: Julien Gobeill, Patrick Ruch, Xin ZhouAbstract:In this paper, we report on query and document expansion using Medical Subject Headings (MeSH) terms designed for Medical ImageCLEF 2008. In this collection, MeSH terms describing an image could be obtained in two different ways: either being collected with the associated MEDLINE's paper, or being extracted from the associated caption. We compared document expansion using both. From a baseline of 0.136 for Mean Average Precision (MAP), we reached a MAP of respectively 0.176 (+29%) with the first method, and 0.154 (+13%) with the second. In-depth analyses show how both strategies were beneficial, as they covered different aspects of the image. Finally, we combined them in order to produce a significantly better run (0.254 MAP, +86%). Combining the MeSH terms using both methods gives hence a better representation of the images, in order to perform document expansion.
Teresa M Salgado - One of the best experts on this subject based on the ideXlab platform.
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redefining the pharmacology and pharmacy Subject category in the journal citation reports using Medical Subject Headings mesh
International Journal of Clinical Pharmacy, 2017Co-Authors: Fernando Minguet, Teresa M Salgado, Claudio Santopadre, Fernando FernandezllimosAbstract:Background The Journal Citation Reports (JCR) Pharmacology and Pharmacy Subject category is heterogeneous. The inclusion of journals with basic and clinical scopes, which have different citation patterns, compromises comparability of impact factors among journals within the category. Objective To subdivide the Pharmacology and Pharmacy category into basic pharmacology, clinical pharmacology, and pharmacy based on the analyses of Medical Subject Headings (MeSH) as a proxy of journals’ scopes. Setting JCR. Method All articles, and respective MeSH, published in 2013, 2014, and 2015 in all journals included in the 2014 JCR Pharmacology and Pharmacy category were retrieved from PubMed. Several models using a combination of the 14 MeSH categories and specific MeSH tree branches were tested using hierarchical cluster analysis. Main outcome measure Distribution of journals across the subcategories of the JCR Pharmacology and Pharmacy Subject category. Results A total of 107,847 articles from 214 journals were included. Nine different models combining the MeSH categories M (Persons) and N (Health Care) with specific MeSH tree branches (selected ad-hoc) and Pharmacy-specific MeSH (identified in previous research) consistently grouped 142 journals (66.4%) in homogeneous groups reflecting their basic and clinical pharmacology, and pharmacy scopes. Ultimately, journals were clustered into: 150 in basic pharmacology, 43 in clinical pharmacology, 16 in basic pharmacology and clinical pharmacology, and 5 in pharmacy. Conclusion The reformulation of the Pharmacology and Pharmacy category into three categories was demonstrated by the consistent results obtained from testing nine different clustering models using the MeSH terms assigned to their articles.
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new pharmacy specific Medical Subject Headings included in the 2017 database
American Journal of Health-system Pharmacy, 2017Co-Authors: Fernando Fernandezllimos, Fernando Minguet, Teresa M SalgadoAbstract:The generation of a robust body of evidence starts with the identification of high-quality studies that can be subsequently synthesized in systematic reviews and meta-analyses. “Systematic reviews need systematic searchers” who use specific procedures and instruments to ensure the quality of
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quality of pharmacy specific Medical Subject Headings mesh assignment in pharmacy journals indexed in medline
Research in Social & Administrative Pharmacy, 2015Co-Authors: Fernando Minguet, Lucienne Van Den Boogerd, Teresa M Salgado, Fernando FernandezllimosAbstract:Abstract Background The Medical Subject Headings (MeSH) is the National Library of Medicine (NLM) controlled vocabulary for indexing articles. Inaccuracies in the MeSH thesaurus have been reported for several areas including pharmacy. Objectives To assess the quality of pharmacy-specific MeSH assignment to articles indexed in pharmacy journals. Methods The 10 journals containing the highest number of articles published in 2012 indexed under the MeSH ‘Pharmacists' were identified. All articles published over a 5-year period (2008–2012) in the 10 previously selected journals were retrieved from PubMed. MeSH terms used to index these articles were extracted and pharmacy-specific MeSH terms were identified. The frequency of use of pharmacy-specific MeSH terms was calculated across journals. Results A total of 6989 articles were retrieved from the 10 pharmacy journals, of which 328 (4.7%) were articles not fully indexed and therefore did not contain any MeSH terms assigned. Among the 6661 articles fully indexed, the mean number of MeSH terms was 10.1 (SD = 4.0), being 1.0 (SD = 1.3) considered as Major MeSH. Both values significantly varied across journals. The mean number of pharmacy-specific MeSH terms per article was 0.9 (SD = 1.2). A total of 3490 (52.4%) of the 6661 articles were indexed in pharmacy journals without a single pharmacy-specific MeSH. Of the total 67193 MeSH terms assigned to articles, on average 10.5% (SD = 13.9) were pharmacy-specific MeSH. A statistically significant different pattern of pharmacy-specific MeSH assignment was identified across journals (Kruskal–Wallis P Conclusions The quality of assignment of the existing pharmacy-specific MeSH terms to articles indexed in pharmacy journals can be improved to further enhance evidence gathering in pharmacy. Over half of the articles published in the top-10 journals publishing pharmacy literature were indexed without a single pharmacy-specific MeSH.
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characterization of the Medical Subject Headings thesaurus for pharmacy
American Journal of Health-system Pharmacy, 2014Co-Authors: Fernando Minguet, Lucienne Van Den Boogerd, Teresa M Salgado, Cassyano Januario Correr, Fernando FernandezllimosAbstract:Purpose The completeness and utility of pharmacy-oriented Medical Subject Headings (MeSH) relative to MeSH terminology pertaining to other healthcare professions (dentistry and nursing) are evaluated. Methods The 2013 version of the MeSH thesaurus—the standard vocabulary used by the National Library of Medicine (NLM) to index articles in PubMed and MEDLINE—was searched for dentistry-, nursing-, and pharmacy-specific terms using a truncation strategy (search terms: nurs*, dent*, and pharm* ); the hierarchical level of each term and the number of descendant terms (an indication of the granularity of the associated NLM-indexed content) were determined. PubMed searches were conducted to identify areas of the MeSH hierarchy containing dentistry- and nursing-specific terms but no equivalent pharmacy-specific term. Results The search of the MeSH thesaurus identified 145 terms representing dentistry-specific activities and 94 and 26 terms specific to nursing and pharmacy practice, respectively. Analysis of the three sets of MeSH terms indicated that dentistry-oriented MeSH terms were generally situated more prominently within the MeSH hierarchy than terms for nursing- and pharmacy-oriented research; the MeSH terminology oriented toward nursing or dentistry practice was relatively more granular, allowing for increased specificity and power of information retrieval during PubMed and MEDLINE searches. Seventeen proposed new MeSH terms describing key areas of pharmacy practice were identified; the inclusion of these terms in the MeSH hierarchy could substantially expand and improve the retrievability of NLM-indexed literature. Conclusion Imbalances and gaps were found in MeSH coverage of pharmacy concepts and terminology relative to MeSH terminology specific to the nursing and dentistry professions.
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identification of pharmacy specific Medical Subject Headings mesh lacking in the mesh thesaurus
Research in Social & Administrative Pharmacy, 2014Co-Authors: Fernando Fernandezllimos, Fernando Minguet, Lucienne Van Den Boogerd, Teresa M SalgadoAbstract:brand names, their compliance with the current pharmaceutical regulations and with the European Portuguese linguistic systems. Methods:Descriptive analytical study of a representative sample (N1⁄4474) of brands, selected from Portuguese National Medicines Formulary. Firstly, the linguistic evaluation was performed through a dedicated software comprising the quantification of words, letters and syllables in each brand, and including the identification of names with abbreviations and hyphens. Secondly, each brand was individually assessed in relation to their compliance with linguistic rules (e.g. letters or word segments that do not exist in Portuguese), as well as with pharmaceutical regulations (e.g. use of abbreviations). Results: Overall, 166 (35.1%) names were found not to be written in accordance with the orthographic Portuguese system. From the 308 compliant names, 124 (26.2%) included structures of very low frequency in current Portuguese (e.g. names finishing with “x”). The overall average number of letters by name was 8.7 ( 3.2), while 95.6% of the names were composed by less than 4 syllables, in line with general Portuguese. The overall average number of words by name was 1.3 ( 0.7) and there were 20 (4.2%) names presenting hyphens and 36 (7.6%) with abbreviations, which is noncompliant with pharmaceutical regulations. Additionally, 20 brands presented other regulatory issues, such as the use of words “rapid” or “hyper”. Conclusions: The high prevalence of names inspired by foreign languages, which are often deviant from the phonological and orthographic Portuguese systems, bring forward readability concerns. Challenging names suggest difficulties on medicines’ memorization and communication, particularly by low literate patients. Medicines’ brand names should not only follow marketing needs but also the purpose of being completely legible, improving medicines’ safe use.