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At least 1,477 records · Page 82Linked to original sources
PubMed1993

Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment.

BACKGROUND AND PURPOSE: The etiology of ischemic stroke affects prognosis, outcome, and management. Trials of therapies for patients with acute stroke should include measurements of responses as influenced by subtype of ischemic stroke. A system for categorization of subtypes of ischemic stroke mainly based on etiology has been developed for the Trial of Org 10172 in Acute Stroke Treatment (TOAST). METHODS: A classification of subtypes was prepared using clinical features and the results of ancillary diagnostic studies. "Possible" and "probable" diagnoses can be made based on the physician's certainty of diagnosis. The usefulness and interrater agreement of the classification were tested by two neurologists who had not participated in the writing of the criteria. The neurologists independently used the TOAST classification system in their bedside evaluation of 20 patients, first based only on clinical features and then after reviewing the results of diagnostic tests. RESULTS: The TOAST classification denotes five subtypes of ischemic stroke: 1) large-artery atherosclerosis, 2) cardioembolism, 3) small-vessel occlusion, 4) stroke of other determined etiology, and 5) stroke of undetermined etiology. Using this rating system, interphysician agreement was very high. The two physicians disagreed in only one patient. They were both able to reach a specific etiologic diagnosis in 11 patients, whereas the cause of stroke was not determined in nine. CONCLUSIONS: The TOAST stroke subtype classification system is easy to use and has good interobserver agreement. This system should allow investigators to report responses to treatment among important subgroups of patients with ischemic stroke. Clinical trials testing treatments for acute ischemic stroke should include similar methods to diagnose subtypes of stroke.

Anticoagulants↗
PubMed1993

Interobserver reliability of a clinical classification of acute cerebral infarction.

BACKGROUND AND PURPOSE: The Oxfordshire Community Stroke Project (OCSP) clinical classification of subtypes of cerebral infarction (total and partial anterior circulation infarction, lacunar infarction, and posterior circulation infarction) can be used to predict early mortality, functional outcome, and whether the infarct was likely due to large- or small-vessel occlusion. The OCSP classification was originally developed and tested by neurologists as part of a community-based study of first-ever stroke, in which some cases were seen after the acute phase. We examined the interobserver reliability of the classification when used in everyday clinical practice in patients seen during the acute phase of stroke shortly after admission to the hospital. METHODS: Two clinicians independently assessed consecutive patients admitted to the hospital with an acute stroke and recorded both the neurological features and their opinion of the subtype of infarct. RESULTS: Eighty-five patients were assessed. Interobserver agreement for the classification was moderate to good (kappa = 0.54; 95% confidence interval, 0.39 to 0.68). Differences in the assessment of the commonly elicited neurological signs explained many of the disagreements: interobserver agreement was good for some signs (hemiparesis [kappa = 0.77], dysphasia [kappa = 0.70]), moderate for some (hemianopia [kappa = 0.39]), and poor for others (sensory loss [kappa = 0.15]). CONCLUSIONS: The classification was simple and practicable (and could be widely used in routine clinical practice, randomized controlled trials, and audit), and interobserver reliability was satisfactory.

Acute Disease↗
PubMed1995

Interrater reliability of an etiologic classification of ischemic stroke.

BACKGROUND AND PURPOSE: Precise identification of the cause of stroke is critical to research and clinical practice. Published series of ischemic stroke show considerable variation in the proportion of cases classified as atherosclerotic large-vessel disease, lacunar infarct, cardioembolic stroke, stroke of other known cause, and stroke of undetermined etiology. We describe the development and use of an etiology-specific classification of ischemic stroke. The interrater reliability of the classification is then evaluated. METHODS: A total of 160 cases of ischemic strokes in young adults were reviewed by paired neurologists who assigned cases to prioritized categories. The results of paired ratings were evaluated for each of the potential causes. Interrater agreement was assessed by means of kappa, which is the chance-adjusted percent agreement. RESULTS: For standard pairs, kappa was fair to good for all causes except lacunar stroke (kappa = 0.31); however, pair-to-pair variation was greatest for lacunar strokes. Strokes of undetermined cause and hematologic/other cause were of borderline fair reliability. CONCLUSIONS: The utility of a stroke classification system is dependent on its intended use. An etiologic classification is useful in studies of the epidemiology and pathophysiological basis of stroke. Fair to good reliability for an etiologic classification of stroke can be obtained when criteria are explicit.

Adolescent↗
PubMed2001

Improving the reliability of stroke subgroup classification using the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria.

BACKGROUND AND PURPOSE: We sought to improve the reliability of the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification of stroke subtype for retrospective use in clinical, health services, and quality of care outcome studies. The TOAST investigators devised a series of 11 definitions to classify patients with ischemic stroke into 5 major etiologic/pathophysiological groupings. Interrater agreement was reported to be substantial in a series of patients who were independently assessed by pairs of physicians. However, the investigators cautioned that disagreements in subtype assignment remain despite the use of these explicit criteria and that trials should include measures to ensure the most uniform diagnosis possible. METHODS: In preparation for a study of outcomes and management practices for patients with ischemic stroke within Department of Veterans Affairs hospitals, 2 neurologists and 2 internists first retrospectively classified a series of 14 randomly selected stroke patients on the basis of the TOAST definitions to provide a baseline assessment of interrater agreement. A 2-phase process was then used to improve the reliability of subtype assignment. In the first phase, a computerized algorithm was developed to assign the TOAST diagnostic category. The reliability of the computerized algorithm was tested with a series of synthetic cases designed to provide data fitting each of the 11 definitions. In the second phase, critical disagreements in the data abstraction process were identified and remaining variability was reduced by the development of standardized procedures for retrieving relevant information from the medical record. RESULTS: The 4 physicians agreed in subtype diagnosis for only 2 of the 14 baseline cases (14%) using all 11 TOAST definitions and for 4 of the 14 cases (29%) when the classifications were collapsed into the 5 major etiologic/pathophysiological groupings (kappa=0.42; 95% CI, 0.32 to 0.53). There was 100% agreement between classifications generated by the computerized algorithm and the intended diagnostic groups for the 11 synthetic cases. The algorithm was then applied to the original 14 cases, and the diagnostic categorization was compared with each of the 4 physicians' baseline assignments. For the 5 collapsed subtypes, the algorithm-based and physician-assigned diagnoses disagreed for 29% to 50% of the cases, reflecting variation in the abstracted data and/or its interpretation. The use of an operations manual designed to guide data abstraction improved the reliability subtype assignment (kappa=0.54; 95% CI, 0.26 to 0.82). Critical disagreements in the abstracted data were identified, and the manual was revised accordingly. Reliability with the use of the 5 collapsed groupings then improved for both interrater (kappa=0.68; 95% CI, 0.44 to 0.91) and intrarater (kappa=0.74; 95% CI, 0.61 to 0.87) agreement. Examining each remaining disagreement revealed that half were due to ambiguities in the medical record and half were related to otherwise unexplained errors in data abstraction. CONCLUSIONS: Ischemic stroke subtype based on published TOAST classification criteria can be reliably assigned with the use of a computerized algorithm with data obtained through standardized medical record abstraction procedures. Some variability in stroke subtype classification will remain because of inconsistencies in the medical record and errors in data abstraction. This residual variability can be addressed by having 2 raters classify each case and then identifying and resolving the reason(s) for the disagreement.

Acute Disease↗
PubMed2004

Learning classification in the olfactory system of insects.

We propose a theoretical framework for odor classification in the olfactory system of insects. The classification task is accomplished in two steps. The first is a transformation from the antennal lobe to the intrinsic Kenyon cells in the mushroom body. This transformation into a higher-dimensional space is an injective function and can be implemented without any type of learning at the synaptic connections. In the second step, the encoded odors in the intrinsic Kenyon cells are linearly classified in the mushroom body lobes. The neurons that perform this linear classification are equivalent to hyperplanes whose connections are tuned by local Hebbian learning and by competition due to mutual inhibition. We calculate the range of values of activity and size of the network required to achieve efficient classification within this scheme in insect olfaction. We are able to demonstrate that biologically plausible control mechanisms can accomplish efficient classification of odors.

Animals↗
PubMed2001

Agreement between alternative classifications of acute respiratory distress syndrome.

To examine the agreement between two classifications of acute respiratory distress syndrome (ARDS) that are used interchangeably in clinical practice and clinical research, we classified 118 patients taking part in a randomized trial with respect to the presence of ARDS using the North American-European Consensus Committee (NAECC) and the Lung Injury Severity Score (LISS) criteria. The incidence of ARDS using NAECC criteria was 55.1% (95% confidence interval, 46.1% to 64.1%), and using the LISS criteria 61.9% (95% confidence interval, 53.1% to 70.6%). The p value on the difference between these proportions was 0.07. Raw agreement, chance-corrected agreement (kappa), and chance-independent agreement (phi) on the study occurrence of ARDS using the two classifications were, respectively, 0.73 (95% CI, 0.65 to 0.81), 0.46 (95% CI, 0.32 to 0.61), and 0.63 (95% CI, 0.41 to 0.79). No single component of either index contributed to disagreement to an appreciably greater extent than other components. Baseline characteristics and outcomes were similar among patients who developed ARDS according to either classification. We conclude that NAECC and LISS classifications resulted in similar estimates of the incidence of ARDS in this clinical trial, though patients were frequently classified as having ARDS with only one model. These discordant classifications had no prognostic importance.

Adult↗
PubMed1997

Predictive validity of the Braunwald classification of unstable angina for angiographic findings, short-term prognoses, and treatment selection.

The authors tested the Braunwald classification for its predictive validity for underlying coronary conditions, clinical courses, and responses to treatment. A reliable definition and classification of unstable angina is needed to help physicians make correct diagnoses of patients' conditions and to appraise findings from clinical trials critically. Many clinical trials have been conducted, but it is difficult to compare the results because of different entry criteria. Of 113 consecutive patients admitted with unstable angina, 89 who had primary angina were studied. Braunwald's classification was applied at admission. The outcomes of interest during hospitalization were coronary angiographic findings, short-term prognoses, and the treatment selected. Multivariate analysis showed that the severity class expressed significant positive predictivity for coronary thrombi (adjusted odds ratio [OR], 6.53; 95% confidence interval [CI], 2.82 to 15.1) and progress to impending infarction (OR, 10.43; CI, 3.35 to 32.49). The treatment (OR, 0.02; CI, 0.004 to 0.08) and electrocardiographic (OR, 0.22; CI 0.10 to 0.49) classes showed independent negative predictivity for coronary vasospasm. The treatment (OR, 3.50; CI, 1.94 to 6.33) and electrocardiographic (odds ratio, 3.27; CI, 1.87 to 5.71) classes showed positive predictivity for the necessity for recanalization treatment with coronary angioplasty or bypass grafting. The Braunwald classification used at admission is highly predictive of underlying coronary conditions, progression to impending infarction, and the final selection of treatment. This classification should be considered in determining patient eligibility in clinical trials and studies.

Adult↗
PubMed1997

Algorithm analysis of lectin glycohistochemistry and Feulgen cytometry for a new classification of nasal polyposis.

The aim of this study is to present a new classification of nasal polyps. This classification is based both on morphologic criteria relating to morphonuclear features from isolated Feulgen-stained nuclei and on glycohistochemical characteristics from histologic slides submitted to three lectins (peanut, wheat germ, and gorse seed agglutinins) and one neoglycoconjugate glycohistochemical stain. While the morphonuclear features (including 30 variables) relate essentially to chromatin pattern, the glycohistochemical stains (including 16 variables) are linked to the presence of specific carbohydrate moieties in cell membranes and cytoplasm. Forty-nine nasal polyps, including single polyps, diffuse polyposis, cystic fibrosis-related polyposis, and aspirin idiosyncracy-related polyposis associated with asthma, were thus characterized. All the variables were obtained quantitatively by means of computer-assisted microscopy. Two complementary methods of data classification were used to determine the actual diagnostic value contributed by each quantitative variable, namely, discriminant analysis, which forms part of multifactorial statistical analysis, and the decision tree technique, which is an artificial intelligence-related algorithm. The data so obtained show that our morphologic classification of nasal polyps fits in with the classification of nasal polyps defined on the basis of clinical criteria.

Algorithms↗
PubMedDate not supplied

Classification of the degree of obesity by body mass index or by deviation from ideal weight.

BACKGROUND: The purpose of this study was to compare classifications of subjects as underweight, normal weight, or obese by body mass index (BMI) and the ratio of body weight to ideal weight (W/IW). METHODS: We performed a theoretical comparison of the 2 indices. We compared classifications of the degree of obesity in 1839 women and 5914 men who were followed up in the primary care clinics of a United States federal hospital. Information was extracted from computerized records. Subjects were classified as underweight (BMI < 18.5 kg/m2, W/IW < 0.9), obese (BMI > or = 30.0 kg/m2, W/IW > or = 1.2), or normal weight (BMI, W/IW values between the cutoff values for underweight and obesity). W/IW values were computed assuming small, medium, and large skeletal frame for all. We compared the classifications of subjects as underweight, normal weight, or obese by BMI and W/IW. We used Cohen's kappa ratio to evaluate the agreement between these classifications. RESULTS: Theoretically, the cutoff values of BMI and W/IW for underweight and obesity are not in agreement. Patient data revealed substantial differences in the classifications of subjects as underweight, normal weight, or obese. Kappa ratios ranged between 0.18 (poor agreement) and 0.71 (reasonable, but not high degree of agreement). In general, kappa ratios were higher when assuming large or medium skeletal frame versus small frame. CONCLUSIONS: There are substantial discrepancies in classifying the subjects of a population as underweight, normal weight, or obese by BMI or W/IW. These discrepancies may cause confusion when 2 or more indices are used simultaneously to classify the degree of obesity.

Body Height↗
PubMedDate not supplied

Improved statistical classification methods in computerized psychiatric diagnosis.

BACKGROUND: Mainstream psychiatric diagnosis involves mainly sequential, expert-system-derived, logical decision rules. Among the few statistical classification methods that have been sporadically evaluated are Bayes, k-nearest neighbor, and discriminant analysis classifiers. METHODS: A statistical classification method based on artificial neural networks (ANN) with task-specific constrained architectures was applied to a sample of 796 clinical interviews, where the symptom evaluation and the diagnostic judgments were made using the Psychiatric State Examination (PSE) system. The proposed constrained ANN (CANN) method was compared with other statistical classification methods. RESULTS: CANN was found to be superior to all other considered methods, having an overall "correct" classification rate of 80% when applied to test data. Similarly, the concordance coefficients of agreement with the PSE diagnostic categories were all very high. Among the other used methods, discriminant analysis had slightly inferior performance but better generalization capability. CONCLUSIONS: The proposed CANN method has a definite utility in psychiatric diagnosis and requires further evaluation, perhaps alongside other standard classification systems and/or with larger samples.

Adult↗
PubMed1983

Transferability of medical decision support systems based on Bayesian classification.

This study tested the hypothesis that probabilities derived from a large, geographically distant data base of stroke patients could form the basis of an accurate Bayesian decision support system for locally predicting the etiology of strokes. Performance of this "extrainstitutional" system on 100 cases was assessed retrospectively, both by error rate and using a new linear accuracy coefficient. This approach to patient classification was found to be surprisingly accurate when compared to classification by physicians and to Bayesian classification based on "low cost" local and subjective probabilities. We conclude that for some medical problems Bayesian classification systems may be significantly more transferable to new sites than is generally believed. Furthermore, this study provides strong support for the utility of clinical databases in building, transferring, and testing Bayesian classification systems in general.

Bayes Theorem↗
PubMed1983

A new multiparametric classification in lung cancer patients - S.M.I.G.

The classification of bronchogenic carcinoma as a function of the prognosis is still an open field. The evaluation of stage, by use of the TNM system, and histologic cell type is not sufficient to guarantee a correct prognosis. The growth rate of the neoplasm is another important parameter. We propose a classification that takes into account the stage (S), histologic cell type (M), immune status (I) and the growth rate of the primary tumor (G): S.M.I.G. We studied 90 lung cancer patients according to the S.M.I.G. classification and we observed that their prognoses were directly correlated with their S.M.I.G. scores (the higher the score, the higher the 10-month mortality rate). The mortality rates within the first 10 months of follow-up were respectively 0%, 0%, 36.36%, 68%, 90.9% for the 5 groups obtained by S.M.I.G. The difference is statistically significant (P less than 0.0075) and there is a linear correlation between the mortality rate and the score assigned to each group (R = 0.943; P less than 0.05). The S.M.I.G. classification can predict the prognosis more efficiently than the usual classification (TNM) and histological cell type.

Adenocarcinoma↗
PubMed2006

Classification of multiple sclerosis patients by latent class analysis of magnetic resonance imaging characteristics.

BACKGROUND: Disease heterogeneity is a major issue in multiple sclerosis (MS). Classification of MS patients is usually based on clinical characteristics. More recently, a pathological classification has been presented. While clinical subtypes differ by magnetic resonance imaging (MRI) signature on a group level, a classification of individual MS patients based purely on MRI characteristics has not been presented so far. OBJECTIVES: To investigate whether a restricted classification of MS patients can be made based on a combination of quantitative and qualitative MRI characteristics and to test whether the resulting subgroups are associated with clinical and laboratory characteristics. METHODS: MRI examinations of the brain and spinal cord of 50 patients were scored for 21 quantitative and qualitative characteristics. Using latent class analysis, subgroups were identified, for whom disease characteristics and laboratory measures were compared. RESULTS: Latent class analysis revealed two subgroups that mainly differed in the extent of lesion confluency and MRI correlates of neuronal loss in the brain. Demographics and disease characteristics were comparable except for cognitive deficits. No correlations with laboratory measures were found. CONCLUSIONS: Latent class analysis offers a feasible approach for classifying subgroups of MS patients based on the presence of MRI characteristics. The reproducibility, longitudinal evolution and further clinical or prognostic relevance of the observed classification will have to be explored in a larger and independent sample of patients.

Adult↗
PubMed1995

Development and Application of a Work-process Classification.

Occupational exposures are related to work processes carried out by the individual worker. A classification of the work processes was developed on the basis of analyses of several databases. Work-process data were collected in a sample of Danish employees. An unambiguous, exhaustive work-process classification was developed where the work process was defined as the transformation of a work object into a product. A test showed that at least 85% of free-text data on occupational injuries contained work-process data. To illustrate applications of the classification, work-process data were used to define highly exposed and unexposed job groups. This classification may be useful for (for example) major general surveys to supplement the exposure information that job and industry classifications yield.

Journal Article↗
PubMed2002

Bethesda proposals for classification of lymphoid neoplasms in mice.

A consensus system for classification of mouse lymphoid neoplasms according to their histopathologic and genetic features has been an elusive target for investigators involved in understanding the pathogenesis of spontaneous cancers or modeling human hematopoietic diseases in mice. An international panel of scientists with expertise in mouse and human hematopathology joined with the hematopathology subcommittee of the Mouse Models for Human Cancers Consortium to develop criteria for definition and classification of these diseases together with a standardized nomenclature. The fundamental elements contributing to the scheme are clinical features, morphology, immunophenotype, and genetic characteristics. The resulting classification has numerous parallels to the World Health Organization classification of human lymphoid tumors while recognizing differences that may be species specific. The classification should facilitate communications about mouse models of human lymphoid diseases.

Animals↗
PubMed2026

Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines.

Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.

Humans↗
PubMed2001

The new World Health Organization classification of lung tumours.

Tumour classification systems provide the foundation for tumour diagnosis and patient therapy and a critical basis for epidemiological and clinical studies. This updated classification was developed with the aim to adhere to the principles of reproducibility, clinical significance, and simplicity in order to minimize the number of unclassifiable lesions. Major changes in the revised classification as compared to the previous one (WHO 1981) include the addition of two pre-invasive lesions to squamous dysplasia and carcinoma in situ; atypical adenomatous hyperplasia and diffuse idiopathic pulmonary neuroendocrine cell hyperplasia. Another change is the subclassification of adenocarcinoma: the definition of bronchioalveolar carcinoma has been restricted to noninvasive tumours. There has been substantial evolution of concepts in neuroendocrine lung tumour classification. Large cell neuroendocrine carcinoma (LCNEC) is now recognized as a histologically high grade non small cell carcinoma showing histopathological features of neuroendocrine differentiation as well as immunohistochemical neuroendocrine markers. The large cell carcinoma class has been enriched with several variants, including the LCNEC and the basaloid carcinoma, both with a dismal prognosis. Finally, a new class was defined called carcinoma with pleomorphic, sarcomatoid, or sarcomatous elements, which brings together a number of proliferations characterized by a spectrum of epithelial to mesenchymal differentiation. Immunohistochemistry and electron microscopy are invaluable techniques for diagnosis and subclassification, but our intention was to render the classification simple and practical to every surgical laboratory, so that most lung tumours could be classified by light microscopic criteria.

Adenocarcinoma↗
PubMed2003

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data.

BACKGROUND: Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant). RESULTS: The proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized. CONCLUSIONS: Our models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.

Biomarkers, Tumor↗