The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform
Soren Jacobsen - One of the best experts on this subject based on the ideXlab platform.
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Inter-tester reliability of a new Diagnostic Classification System for patients with non-specific low back pain
The Australian journal of physiotherapy, 2020Co-Authors: Tom Petersen, Steen Olsen, Mark Laslett, Hanne Thorsen, Claus Manniche, Charlotte Ekdahl, Soren JacobsenAbstract:Most patients referred to physiotherapy with low back pain are without a precise medical diagnosis. Identification of subgroups of non-specific low back pain patients may improve clinical outcomes and research efficiency. A pathoanatomic Classification System has been developed, classifying patients with non-specific low back pain into 12 different syndromes and three subcategories based on history and physical examination. The purpose of this study was to estimate the inter-tester reliability of clinical tests used as criteria for classifying patients. Ninety patients with chronic low back pain were each examined by two physiotherapists. A total of four physiotherapists conducted the assessments. Examination findings were recorded independently by the two examiners. Percentage of agreement and kappa coefficients were calculated for each category. The overall rate of agreement was 72% and the kappa coefficient was 0.62 for the mutually exclusive syndromes in the Classification System. Agreement rates for each of the syndromes ranged from 74% to 100% and kappa coefficients ranged from 0.44 to 1.00. The findings suggest the inter-tester reliability of the System is acceptable. The relatively modest level of total agreement (39%) for the System as a whole might indicate that the utility of the System for general screening purposes is limited, compared with the utility in identification of particular syndromes. Due to low prevalence of positive findings in some of the syndromes, future work should focus on testing reliability on a larger sample of patients, and testing of validity and feasibility of the System.
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inter tester reliability of a new Diagnostic Classification System for patients with non specific low back pain
The Australian journal of physiotherapy, 2004Co-Authors: Tom Petersen, Steen Olsen, Mark Laslett, Hanne Thorsen, Claus Manniche, Charlotte Ekdahl, Soren JacobsenAbstract:6Bispebjerg University Hospital, Denmark Most patients referred to physiotherapy with low back pain are without a precise medical diagnosis. Identification of subgroups of non-specific low back pain patients may improve clinical outcomes and research efficiency. A pathoanatomic Classification System has been developed, classifying patients with non-specific low back pain into 12 different syndromes and three subcategories based on history and physical examination. The purpose of this study was to estimate the inter-tester reliability of clinical tests used as criteria for classifying patients. Ninety patients with chronic low back pain were each examined by two physiotherapists. A total of four physiotherapists conducted the assessments. Examination findings were recorded independently by the two examiners. Percentage of agreement and kappa coefficients were calculated for each category. The overall rate of agreement was 72% and the kappa coefficient was 0.62 for the mutually exclusive syndromes in the Classification System. Agreement rates for each of the syndromes ranged from 74% to 100% and kappa coefficients ranged from 0.44 to 1.00. The findings suggest the inter-tester reliability of the System is acceptable. The relatively modest level of total agreement (39%) for the System as a whole might indicate that the utility of the System for general screening purposes is limited, compared with the utility in identification of particular syndromes. Due to low prevalence of positive findings in some of the syndromes, future work should focus on testing reliability on a larger sample of patients, and testing of validity and feasibility of the System. [Petersen T, Olsen S, Laslett M, Thorsen H, Manniche C, Ekdahl C and Jacobsen S (2004): Inter-tester reliability of a new Diagnostic Classification System for patients with non-specific low back pain. Australian Journal of Physiotherapy 50: 85‐91]
Paddy Power - One of the best experts on this subject based on the ideXlab platform.
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initial diagnosis and treatment in first episode psychosis can an operationalized Diagnostic Classification System enhance treating clinicians diagnosis and the treatment chosen
Early Intervention in Psychiatry, 2011Co-Authors: Ricardo Coentre, Pablo Blanco, Silvina Fontes, Paddy PowerAbstract:Aim: Diagnosis during the initial stages of first-episode psychosis is particularly challenging but crucial in deciding on treatment. This is compounded by important differences in the two major Classification Systems, International Classification of Diseases, 10th revision (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV). We aimed to compare the concordance between an operationalized diagnosis using Operational Criteria Checklist (OPCRIT) and treating clinician-generated diagnosis in first-episode psychosis diagnosis and its correlation with treatment prescribed. Methods: Operationalized polyDiagnostic assessments were conducted on 150 first-episode psychosis patients using OPCRIT. OPCRIT-generated ICD-10, DSM-IV and treating clinician diagnoses were compared. The association between these diagnoses and choice of treatment was evaluated. Results: General agreement between the three Classification Systems was moderate to good, with kappa values between 0.460 and 0.674. There was a higher frequency of schizophrenia diagnosis in ICD-10 (n = 85) comparing to DSM-IV (n = 45) and similar in clinical diagnosis (n = 76), with moderate to good agreement between Classifications (kappa between 0.602 and 0.731). No significant differences were found for ratings of psychotic depressive and manic/bipolar disorders with psychosis, with affective disorders having the higher agreement. Heterogeneous group of ‘other disorders’ achieved a kappa value from 0.250 (DSM-IV/ICD-10) to 0.566 (DSM-IV/clinical diagnosis). Conclusion: Despite the challenges in first-episode psychosis diagnosis, it is possible to have a good agreement between OPCRIT-generated (DSM-IV and ICD-10) diagnoses and clinician-based diagnoses, although some differences exist. The choice of psychopharmacological treatment prescribed matches well with these operationalized diagnoses.
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Initial diagnosis and treatment in first‐episode psychosis: can an operationalized Diagnostic Classification System enhance treating clinicians' diagnosis and the treatment chosen?
Early Intervention in Psychiatry, 2011Co-Authors: Ricardo Coentre, Pablo Blanco, Silvina Fontes, Paddy PowerAbstract:Aim: Diagnosis during the initial stages of first-episode psychosis is particularly challenging but crucial in deciding on treatment. This is compounded by important differences in the two major Classification Systems, International Classification of Diseases, 10th revision (ICD-10) and Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV). We aimed to compare the concordance between an operationalized diagnosis using Operational Criteria Checklist (OPCRIT) and treating clinician-generated diagnosis in first-episode psychosis diagnosis and its correlation with treatment prescribed. Methods: Operationalized polyDiagnostic assessments were conducted on 150 first-episode psychosis patients using OPCRIT. OPCRIT-generated ICD-10, DSM-IV and treating clinician diagnoses were compared. The association between these diagnoses and choice of treatment was evaluated. Results: General agreement between the three Classification Systems was moderate to good, with kappa values between 0.460 and 0.674. There was a higher frequency of schizophrenia diagnosis in ICD-10 (n = 85) comparing to DSM-IV (n = 45) and similar in clinical diagnosis (n = 76), with moderate to good agreement between Classifications (kappa between 0.602 and 0.731). No significant differences were found for ratings of psychotic depressive and manic/bipolar disorders with psychosis, with affective disorders having the higher agreement. Heterogeneous group of ‘other disorders’ achieved a kappa value from 0.250 (DSM-IV/ICD-10) to 0.566 (DSM-IV/clinical diagnosis). Conclusion: Despite the challenges in first-episode psychosis diagnosis, it is possible to have a good agreement between OPCRIT-generated (DSM-IV and ICD-10) diagnoses and clinician-based diagnoses, although some differences exist. The choice of psychopharmacological treatment prescribed matches well with these operationalized diagnoses.
Daniel H Lowenstein - One of the best experts on this subject based on the ideXlab platform.
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a definition and Classification of status epilepticus report of the ilae task force on Classification of status epilepticus
Epilepsia, 2015Co-Authors: Eugen Trinka, Hannah R Cock, Dale C Hesdorffer, Andrea O Rossetti, Ingrid E Scheffer, Shlomo Shinnar, S D Shorvon, Daniel H LowensteinAbstract:Summary The Commission on Classification and Terminology and the Commission on Epidemiology of the International League Against Epilepsy (ILAE) have charged a Task Force to revise concepts, definition, and Classification of status epilepticus (SE). The proposed new definition of SE is as follows: Status epilepticus is a condition resulting either from the failure of the mechanisms responsible for seizure termination or from the initiation of mechanisms, which lead to abnormally, prolonged seizures (after time point t1). It is a condition, which can have long-term consequences (after time point t2), including neuronal death, neuronal injury, and alteration of neuronal networks, depending on the type and duration of seizures. This definition is conceptual, with two operational dimensions: the first is the length of the seizure and the time point (t1) beyond which the seizure should be regarded as “continuous seizure activity.” The second time point (t2) is the time of ongoing seizure activity after which there is a risk of long-term consequences. In the case of convulsive (tonic–clonic) SE, both time points (t1 at 5 min and t2 at 30 min) are based on animal experiments and clinical research. This evidence is incomplete, and there is furthermore considerable variation, so these time points should be considered as the best estimates currently available. Data are not yet available for other forms of SE, but as knowledge and understanding increase, time points can be defined for specific forms of SE based on scientific evidence and incorporated into the definition, without changing the underlying concepts. A new Diagnostic Classification System of SE is proposed, which will provide a framework for clinical diagnosis, investigation, and therapeutic approaches for each patient. There are four axes: (1) semiology; (2) etiology; (3) electroencephalography (EEG) correlates; and (4) age. Axis 1 (semiology) lists different forms of SE divided into those with prominent motor Systems, those without prominent motor Systems, and currently indeterminate conditions (such as acute confusional states with epileptiform EEG patterns). Axis 2 (etiology) is divided into subcategories of known and unknown causes. Axis 3 (EEG correlates) adopts the latest recommendations by consensus panels to use the following descriptors for the EEG: name of pattern, morphology, location, time-related features, modulation, and effect of intervention. Finally, axis 4 divides age groups into neonatal, infancy, childhood, adolescent and adulthood, and elderly.
Amedeo Minichino - One of the best experts on this subject based on the ideXlab platform.
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prediction of functional outcome in young patients with a recent onset psychiatric disorder beyond the traditional Diagnostic Classification System
Schizophrenia Research, 2017Co-Authors: Amedeo Minichino, Marta Francesconi, Ricardo E Carrion, Arturo Bevilacqua, Maurizio Parisi, Santo Rullo, Agata Ando, Massimo BiondiAbstract:Abstract A critical research goal is to identify modifiable risk factors leading to functional disabilities in young psychiatric patients. The authors developed a multidimensional trans-Diagnostic predictive model of functional outcome in patients with the recent-onset of a psychiatric illness. Baseline clinical, psychosis-risk status, cognitive, neurological-soft-signs measures, and dopamine-related-gene polymorphisms (DRD1-rs4532, COMT-rs165599, and DRD4-rs1800955) were collected in 138 young non-psychotic outpatients. 116 individuals underwent follow-up (mean = 2.2 years, SD = 0.9) examination. A binary logistic model was used to predict low-functioning status at follow-up as defined by a score lower than 65 in the social occupational functioning assessment scale. A total of 54% of patients experiences low functioning at follow-up. Attention, Avolition, and Motor-Coordination subscale were significant predictors of low-functioning with an accuracy of 79.7%. A non-significant trend was found for a dopamine-related-gene polymorphism (DRD1-rs4532). The model was independent of psychotic-risk status, DSM-diagnosis, and psychotic conversion. A trans-Diagnostic approach taking into account specific neurocognitive, clinical, and neurological information has the potential to identify those individuals with low-functioning independent of DSM diagnosis or the level of psychosis-risk. Specific early interventions targeting modifiable risk factors and emphasize functional recovery in young psychiatric samples, independent of DSM-diagnosis and psychosis-risk, are essential.
Chein-i Chang - One of the best experts on this subject based on the ideXlab platform.
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An automatic Diagnostic System for CT liver image Classification
IEEE Transactions on Biomedical Engineering, 1998Co-Authors: E-liang Chen, Pau-choo Chung, Ching-liang Chen, Hong-ming Tsai, Chein-i ChangAbstract:Computed tomography (CT) images have been widely used for liver disease diagnosis. Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, a CT liver image Diagnostic Classification System is presented which will automatically find, extract the CT liver boundary and further classify liver diseases. The System comprises a detect-before-extract (DBE) System which automatically finds the liver boundary and a neural network liver classifier which uses specially designed feature descriptors to distinguish normal liver, two types of liver tumors, hepatoma and hemageoma. The DBE System applies the concept of the normalized fractional Brownian motion model to find an initial liver boundary and then uses a deformable contour model to precisely delineate the liver boundary. The neural network is included to classify liver tumors into hepatoma and hemageoma. It is implemented by a modified probabilistic neural network (PNN) [MPNN] in conjunction with feature descriptors which are generated by fractal feature information and the gray-level co-occurrence matrix. The proposed System was evaluated by 30 liver cases and shown to be efficient and very effective.
