[JPS 5th ed. Classification of pancreatic cancer and JPS classification versus UICC classification].
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Humans↗
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Results for “CLASSIFICATION”
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[Classification of pancreatitis--comparison of the revised 1984 Marseille Classification and the 1983 Cambridge classification].
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Acute Disease↗
[Angina pectoris: classification and clinical significance--the AHA classification and WHO classification].
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American Heart Association↗
An international classification of retinopathy of prematurity. II. The classification of retinal detachment. The International Committee for the Classification of the Late Stages of Retinopathy of Prematurity.
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Humans↗
[Comparative study of traditional classification, numerical classification and classification by DNA homology].
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DNA, Bacterial↗
[Radiographic classification of esophageal neoplasms--type classification to Bormann's classification].
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Esophageal Neoplasms↗
A classification of cardiac allograft rejection. A modification of the classification by Billingham.
We herein propose a classification of rejection in cardiac allografts based on the original Stanford work. Our modified classification, as a work hypothesis, defines the following grades: mild acute rejection (A-1), corresponding to Billingham's "mild rejection"; mild acute rejection with probable conversion to moderate rejection (A-2); moderate acute rejection (A-3), comparable to Billingham's "moderate rejection"; and severe acute rejection (A-4), morphologically identical with the respective grade in the Billingham classification. The resolution of rejection has been classified into two grades--early (A-5a) and late (A-5b) resolution--according to the development of granulation tissues. We also grade the degree of vasculopathy (B-1, B-2) and chronic rejection (C), which is characterized by aggressive fibrosis and persistent vasculopathy. Mild rejection with possible conversion to moderate rejection is defined by an increasing quantity of retrogressive changes in myocytes. Changes not related to transplantation are characterized in our classification by descriptive diagnosis. The proposed classification was validated by 1 year of clinical experience and by the evaluation of possible prognostic aspects of the classification.
Biopsy↗
Molecular classification of human diffuse gliomas by multidimensional scaling analysis of gene expression profiles parallels morphology-based classification, correlates with survival, and reveals clinically-relevant novel glioma subsets.
There are several currently employed classification systems for diffuse gliomas that sort tumors based on histological features. Contemporary molecular techniques, however, offer the promise of improved tumor classification and resultant patient stratification for treatment and prognosis. In particular, gene expression profiling has shown exceptional promise for providing an alternative and more objective molecular approach to glioma classification. In this study, we used cDNA array technology to profile the gene expression of 30 primary human glioma tissue samples comprising 4 different glioma subtypes as defined by current World Health Organization (WHO 2000) criteria: glioblastoma (GM, WHO grade IV), anaplastic astrocytoma (AA, WHO grade III), anaplastic oligodendroglioma (AO, WHO grade III), and oligodendroglioma (OL, WHO grade II). Gene expression data alone were used to group the tumors using multidimensional scaling, which is an unsupervised statistical method. Results show that impressive separation of the 4 glioma subtypes can be achieved solely on the basis of molecular data. In addition, a subcluster of 3 glioblastomas was identified as distinct from other GMs and from the oligodendroglial tumors. These 3 patients have shown extended survival compared to other GMs in the study. Survival analysis of the full data set revealed a good correlation with the molecular classification. Results of this proof-of-principle study demonstrate that molecular profiling alone can recapitulate conventional histologic classification and grading with high fidelity. In addition, results show that the molecular approach to tumor classification can generate clinically meaningful patient stratification, and, more importantly, is an efficient class-discovery tool for human gliomas, permitting the identification of previously unrecognized, clinically relevant tumor subsets.
Algorithms↗
[History of classification systems of conjoined twins by specific consideration of the classification systems of Louis Bolk (1866-1930)].
Classification systems concerning conjoined twins have been developed in the nineteenth century by many authors, a.o. J. F. Meckel (1816), I. Geoffroy Saint-Hilaire (1832), E. F. Gurlt (1831), Fr. Ahlfeld (1880), C. Taruffi (1881), and in the beginning of the twentieth century, a.o. J. W. Ballantyne (1902) and E. Schwalbe (1907). However, these classification systems were very complex and mainly based on the external morphology of the conjoined twins. In contrast to many other classification systems the professor in Anatomy at the University of Amsterdam Louis Bolk divided conjoined twins in only three main groups: 1 greater than diplopagi simplex caudad; 2 greater than diplopagi simplex craniad; 3 greater than diplopagi simplex mesad. The last group was divided into ventrad and laterad conjuncti. For the study of conjoined twins the Vrolik Collection and many other specimens of the Museum of the Department of Anatomy and Embryology of the University of Amsterdam were very important for Bolk. Three factors were the main reason that the concept of the classification of double monsters of Bolk has not been cited often in the international literature: 1 greater than the publications of the classification in a national journal in Dutch; 2 greater than the coincidence of the publication of E. Schwalbes famous handbook "Die Morphologie der Missbildungen des Menschen und der Tiere II. Die Doppelbildungen" in the same period and 3 greater than the problem that the verification of the classification, mainly based upon morphogenesis, has not been possible because of technical problems in performing these experiments in mammals.(ABSTRACT TRUNCATED AT 250 WORDS)
Anatomy↗
Classifying sports medicine diagnoses: a comparison of the International classification of diseases 10-Australian modification (ICD-10-AM) and the Orchard sports injury classification system (OSICS-8).
BACKGROUND: The International classification of diseases 10-Australian modification (ICD-10-AM) and the Orchard sports injury classification system (OSICS-8) are two classifications currently being used in sports injury research. OBJECTIVES: To compare these two systems to determine which was the more reliable and easier to apply in the classification of injury diagnoses of patients who presented to sports physicians in private sports medicine practice. METHODS: Ten sports physicians/sports physician registrars each coded one of 10 different lists of 30 sports medicine diagnoses according to both ICD-10-AM and OSICS-8 in random order. The coders noted the time taken to apply each classification system, and allocated an ease of fit score for individual diagnoses into the systems. The 300 diagnoses were each coded twice more by "expert" coders from each system, and these results compared with those of the 10 volunteers. RESULTS: Overall, there was a higher level of agreement between the different coders for OSICS-8 than for ICD-10-AM. On average, it was 23.5 minutes quicker to complete the task with OSICS-8 than with ICD-10-AM. Furthermore, there was also higher concordance between the three coders with OSICS-8. Subjective analysis of the codes assigned indicated reasons for disagreement and showed that, in some instances, even the "expert" coders had difficulties in assigning the most appropriate codes. CONCLUSIONS: Based on the results of this study, OSICS-8 appears to be the preferred system for use by inexperienced coders in sports medicine research. The agreement between coders was, however, lower than expected. It is recommended that changes be made to both OSICS-8 and ICD-10-AM to improve their reliability for use in sports medicine research.
Abbreviated Injury Scale↗
[Utilization of the International Classification of Primary Care, a new family practice classification].
It is difficult to find an appropriate classification to describe first-line epidemiology adequately. The general practitioner cannot always use the existing classification such as ICD-9-CM: as he is dealing with illness in an early stage, he often uses "hypotheses" and not "diagnoses". The "International Classification of Primary Care", (I.C.P.C.), which has been developed by the World Organisation of National Colleges, Academies and Academic Associations of general practitioners/Family Physicians is one primary health care classification. This classification has two axes with 17 chapters and 7 components. It classifies the reason for encounter, the diagnosis as well as the interventions of the general practitioner. The author reports on a first-trial using I.C.P.C. in Belgium. By means of participating observation by student-trainees, 5.478 encounters between general practitioner and patient of 94 general practitioners have been registered and centrally encoded. The possibilities to analyse the data are illustrated. At the same time, the author developed a nomenclature of medicine (6-figure code) which has been integrated in the I.C.P.C. Finally, the importance of this kind of epidemiologic first-line research has been pointed out.
Belgium↗
Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation.
The classification of diabetes mellitus and the tests used for its diagnosis were brought into order by the National Diabetes Data Group of the USA and the second World Health Organization Expert Committee on Diabetes Mellitus in 1979 and 1980. Apart from minor modifications by WHO in 1985, little has been changed since that time. There is however considerable new knowledge regarding the aetiology of different forms of diabetes as well as more information on the predictive value of different blood glucose values for the complications of diabetes. A WHO Consultation has therefore taken place in parallel with a report by an American Diabetes Association Expert Committee to re-examine diagnostic criteria and classification. The present document includes the conclusions of the former and is intended for wide distribution and discussion before final proposals are submitted to WHO for approval. The main changes proposed are as follows. The diagnostic fasting plasma (blood) glucose value has been lowered to > or =7.0 mmol l(-1) (6.1 mmol l(-1)). Impaired Glucose Tolerance (IGT) is changed to allow for the new fasting level. A new category of Impaired Fasting Glycaemia (IFG) is proposed to encompass values which are above normal but below the diagnostic cut-off for diabetes (plasma > or =6.1 to <7.0 mmol l(-1); whole blood > or =5.6 to <6.1 mmol l(-1)). Gestational Diabetes Mellitus (GDM) now includes gestational impaired glucose tolerance as well as the previous GDM. The classification defines both process and stage of the disease. The processes include Type 1, autoimmune and non-autoimmune, with beta-cell destruction; Type 2 with varying degrees of insulin resistance and insulin hyposecretion; Gestational Diabetes Mellitus; and Other Types where the cause is known (e.g. MODY, endocrinopathies). It is anticipated that this group will expand as causes of Type 2 become known. Stages range from normoglycaemia to insulin required for survival. It is hoped that the new classification will allow better classification of individuals and lead to fewer therapeutic misjudgements.
Autoimmune Diseases↗
Classification of rhabdomyosarcomas and related sarcomas. Pathologic aspects and proposal for a new classification--an Intergroup Rhabdomyosarcoma Study.
BACKGROUND: There is a need to develop a single prognostically significant classification of rhabdomyosarcomas (RMS) and other related tumors of children, adolescents, and young adults which would be a current guide for their diagnosis, allow valid comparison of outcomes between protocols carried out anywhere in the world, and should enhance recognition of prognostic subsets. METHOD: Sixteen pathologists from eight pathology groups, representing six countries and several cooperative groups, classified by four histopathologic classification schemes 800 representative tumors of the 999 eligible cases treated on Intergroup Rhabdomyosarcoma Study II. Each tumor was classified according to each of the four systems by each of the pathologists. In addition, two independent subsamples of 200 of the 800 patients were reviewed according to the new system, so that 343 distinct patients were reviewed once, and 57 of these twice. RESULTS: A study of the survival rates of all subtypes in the sample of 800 patients led to the formation of a new system. This was tested on two independent subsets of 200 of the original cases and found to be reproducible and predictive of outcome by univariate analysis. A multivariate analysis of the 343 patients classified according to the new system indicated that a survival model including pathologic classification and known prognostic factors of primary site, clinical group, and tumor size was significantly better at predicting survival than a model with only the known prognostic factors. CONCLUSION: This new classification, termed International Classification of Rhabdomyosarcoma (ICR) by the authors, was reproducible and predictive of outcome among patients with differing histologies treated uniformly on the Intergroup Rhabdomyosarcoma II protocols. We believe it should be utilized by all pathologists and cooperative groups to classify rhabdomyosarcomas in order to provide comparability among and within multi-institutional studies.
Adolescent↗
The molecular genetics and morphometry-based endometrial intraepithelial neoplasia classification system predicts disease progression in endometrial hyperplasia more accurately than the 1994 World Health Organization classification system.
BACKGROUND: The objective of this study was to compare the accuracy of disease progression prediction of the molecular genetics and morphometry-based Endometrial Intraepithelial Neoplasia (EIN) and World Health Organization 1994 (WHO94) classification systems in patients with endometrial hyperplasias. METHODS: A multicenter, multivariate analysis was conducted on 477 patients with endometrial hyperplasia who were required to have a 1-year minimum disease-free interval from the time of the index biopsy (1-18 years of follow-up). The results from that analysis were compared with the results from 197 patients who had < 1 year of follow-up. RESULTS: Twenty-four of 477 hyperplasias (5.0%) progressed to malignant disease over an average of 4 years (maximum, 10 years). According to the WHO94 classification, 16 of 123 atypical hyperplasias (13%) and 8 of 354 nonatypical hyperplasias (2.3%) progressed (hazard ratio [HR] = 7). Twenty-two of 118 EINs (19%) and 2 of 359 non-EINs (0.6%) progressed (HR = 45). EIN was prognostic within each WHO94 subcategory. Progression rates were 3% in simple hyperplasias, 22% in complex hyperplasias, 17% in simple atypical hyperplasias, and 38% in complex atypical hyperplasias with EIN, compared with progression rates of 0.0-2.0% in all hyperplasias if EIN was absent. EIN detected precancerous lesions (sensitivity, 92%) better than WHO94 atypical hyperplasias collectively (67%) or complex atypical hyperplasias alone (46%). In a Cox regression analysis, EIN was the strongest prognostic index of future endometrial carcinoma. The same was true for patients with < 1 year of follow-up (HR for EIN, atypical hyperplasia, and complex atypical hyperplasia: 58, 7, and 8, respectively). CONCLUSIONS: The EIN classification system predicted disease progression more accurately than the WHO94 classification and identified many women with benign changes that would have been regarded as high risk according to the WHO94 classification system.
Adult↗
Protein classification using ontology classification.
MOTIVATION: The classification of proteins expressed by an organism is an important step in understanding the molecular biology of that organism. Traditionally, this classification has been performed by human experts. Human knowledge can recognise the functional properties that are sufficient to place an individual gene product into a particular protein family group. Automation of this task usually fails to meet the 'gold standard' of the human annotator because of the difficult recognition stage. The growing number of genomes, the rapid changes in knowledge and the central role of classification in the annotation process, however, motivates the need to automate this process. RESULTS: We capture human understanding of how to recognise members of the protein phosphatases family by domain architecture as an ontology. By describing protein instances in terms of the domains they contain, it is possible to use description logic reasoners and our ontology to assign those proteins to a protein family class. We have tested our system on classifying the protein phosphatases of the human and Aspergillus fumigatus genomes and found that our knowledge-based, automatic classification matches, and sometimes surpasses, that of the human annotators. We have made the classification process fast and reproducible and, where appropriate knowledge is available, the method can potentially be generalised for use with any protein family. AVAILABILITY: All components described in this paper are freely available. OWL ontology http://www.bioinf.man.ac.uk/phosphabase myGrid http://www.mygrid.org.uk Instance Store http://instancestore.man.ac.uk.
Algorithms↗
NICE, a new cause of death classification for stillbirths and neonatal deaths. Neonatal and Intrauterine Death Classification according to Etiology.
BACKGROUND: Stillbirths and neonatal deaths are often the result of a complicated chain of events. For epidemiological purposes a classification into single cause of death groups is essential. For large-scale studies, a method is needed which enables such grouping based on available register data. METHODS: A cause of death classification system called NICE is presented. It is hierarchical and is aetiologically orientated. A computerized method is adapted which makes use of data in four central Swedish registries. A validation of the computer method has been made from the medical records on a 10% sample of all stillbirths and neonatally dead infants in Sweden from 1983 to 1990. RESULTS: The specificity of the computer method is high, sensitivity is less satisfactory for some subgroups. A time trend analysis illustrates the usefulness of the classification system and shows a decline with time for two groups: placental abruption and obstetric complications. CONCLUSIONS: The NICE classification system fulfils the criteria of an aetiologically orientated classification system which can be used in a computerized environment.
Cause of Death↗
The revised European-American Classification of Lymphoid Neoplasms (REAL): a preferred approach for the classification of cutaneous lymphomas.
The Revised European-American Classification of Lymphoid Neoplasms (REAL) classification is based on the principle that each type of lymphoma is a distinct disease defined by morphology, immunophenotypic and genetic features, clinical presentation, and course. If either primary or secondary involvement of the skin is a constant factor, this aspect is considered integral to disease definition. Organ-specific classification schemes, such as that proposed by the European Organization for Research and Treatment of Cancer (EORTC) for cutaneous lymphomas, are not required, and indeed may impede the recognition of common features of diseases involving multiple anatomic sites. The use of multiple classification systems is a step backward, and may lead to confusion among hematologists/oncologists and dermatologists. Nevertheless, cutaneous lymphomas in many instances are distinct. Their natural history is often more indolent than nodal lymphomas, and for that reason they often require different therapeutic approaches. We agree with the efforts of the EORTC classification to emphasize the unique clinical aspects of many cutaneous lymphomas, as this recognition is essential for appropriate clinical management. As has been learned for nodal lymphomas, clinical features play an important role in prognosis and should be used in guiding therapy. For cutaneous lymphomas, the presence or absence of systemic spread is particularly important.
Hodgkin Disease↗
Classification of chronic radiation sickness cases using neural networks and classification trees.
Chronic radiation sickness is a deterministic radiation health effect observed among the Mayak Production Association workers in Russia. In this study, unsupervised neural networks were used to cluster hematological measurements in a subset (n = 88) of the Mayak Production Association population while excluding from the analysis the radiation dose and the historical clinical diagnosis. Clusters of observations that had lower average leukocyte and thrombocyte counts were labeled "affected" and those having higher average blood cell counts were labeled "unaffected." The class (cluster) membership for each individual was used subsequently as a dependent variable in a classification tree model in order to identify significant features of the underlying classification model. After re-classification of cases using this method, the results showed a better data separation between the blood cell counts for affected vs. unaffected groups compared to those based on historical classification, and a greater difference between group means for differential blood counts was observed than for the historical diagnosis. The reclassification of diagnostic groups changed the group mean radiation doses. The geometric means (and 95% CL) of cumulative radiation dose equivalent from external exposures, based on the historical diagnosis, are 0.31 (0.0035, 3.4) vs. 1.7 (0.0007, 18) Sv. After clustering and classification tree analyses, the group geometric means were 0.78 (0.0014, 8.6) vs. 1.5 (0.0007, 17) and 0.82 (0.0013, 9.0) vs. 1.4 (0.0008, 16) Sv, using (respectively) whole blood cell counts or differential counts as the independent variables. The approach presented here is useful as a diagnostic aid for both retrospective analyses and in the event of future radiation accidents.
Chronic Disease↗