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Back to the future: Valentin Magnan, French psychiatry, and the classification of mental diseases, 1885-1925.

To this day one of the most curious gaps in the historiography of French psychiatry is the era between the fin-de-siècle and the 1920s, years that overlapped the life and career of Valentin Magnan (1835-1916), a pivotal figure in the historical classification of mental diseases. This paper seeks to address this shortcoming as well as contribute to the growing scholarly interest in the history of clinical psychiatry. It argues that Magnan was in many ways a tragic figure, someone who lived and worked at a time when circumstances conspired against him and his efforts to reform psychiatric classification. Essentially Magnan had the misfortune to practise psychiatry when Emil Kraepelin's influence began to spread beyond Germany's borders, sparking a nationalist reaction that penalized both French Kraepelinians and Magnan whose theories shared similarities with Kraepelin's. But Magnan's stature also suffered because of the intense internecine quarrels that arose in late nineteenth-century French psychiatry. Magnan was no helpless victim, though, and there is reason to believe that some of the criticism directed at him was based on documented personal failings. Ultimately, Magnan's theory of psychiatric classification was overtaken by these and other events in French psychiatry, culminating by the interwar period in the emergence of a new national, nosological pardigm that has dominated French psychiatry for most of the twentieth century. Thus Magnan was in many respects a pariah within French psychiatry by the early twentieth century. An examination of his career casts light on this crucial turning-point in the history of French psychiatry and indicates why and how the new model of classification was more to the tastes of his medical colleagues.

Classification↗

15. Canadian experience with patient care classification.

Patient care classification in Canada in the past has been largely dictated by insurance coverage and the fiscal policies of the individual provinces. In recent years, however, the Canadian Department of Health and Welfare has been promoting the development of a standard patient care classification based on assessment of client or patient needs in regard to the category, type, and level of care. Experimentation with the proposed classification system in several provinces confirms the need in long-term care to include assessment of nursing requirements, physical functioning, and psychosocial assets and liabilities, and points to the importance of using such a classification for planning and evaluating patient care as well as for administrative purposes.

Activities of Daily Living↗

Classification of a large microarray data set: algorithm comparison and analysis of drug signatures.

A large gene expression database has been produced that characterizes the gene expression and physiological effects of hundreds of approved and withdrawn drugs, toxicants, and biochemical standards in various organs of live rats. In order to derive useful biological knowledge from this large database, a variety of supervised classification algorithms were compared using a 597-microarray subset of the data. Our studies show that several types of linear classifiers based on Support Vector Machines (SVMs) and Logistic Regression can be used to derive readily interpretable drug signatures with high classification performance. Both methods can be tuned to produce classifiers of drug treatments in the form of short, weighted gene lists which upon analysis reveal that some of the signature genes have a positive contribution (act as "rewards" for the class-of-interest) while others have a negative contribution (act as "penalties") to the classification decision. The combination of reward and penalty genes enhances performance by keeping the number of false positive treatments low. The results of these algorithms are combined with feature selection techniques that further reduce the length of the drug signatures, an important step towards the development of useful diagnostic biomarkers and low-cost assays. Multiple signatures with no genes in common can be generated for the same classification end-point. Comparison of these gene lists identifies biological processes characteristic of a given class.

Algorithms↗

Elementary identification of a gnathosonic classification using an autoregressive model.

This was an investigation to determine the feasibility of an autoregressive (AR) model for establishing characteristic parameters from recorded occlusal sounds and develop their classification. Thirty four normal subjects with intact natural dentitions were selected for the study. The subjects' occlusal sounds from both sides of their faces respectively were sampled, and the gnathosonic classification (Class A, B and C) was established by observing the original recorded wave pattern and measuring the duration. Then, a 20 order AR model was calculated with the collected data, and the AR model coefficients were found to be similar to the indices of Bayes' discriminatory analysis. The total conformation rates of the modelled left and right occlusal sounds to the classification, estimated by Bayes' discriminant functions were 97.06% and 88.24% respectively. AR coefficients representing the characteristics of human occlusal sounds can be helpful in their classification and allow computer diagnosis of occlusal disorders.

Acoustics↗

Perinatal deaths: relevance of Wigglesworth's classification.

Out of a total of 4572 births over a period of 16 months occurring at St Philomena's Hospital, Bangalore, India, which has level II nursery facilities, there were 196 perinatal deaths. Perinatal mortality was 42.9/1000 total births. Case fatality rate was 12.4% for those born with a birthweight between 1501 to 2000 g, 35.5% for those between 1001 to 1500 g and 100% for those less than 1001 g. These deaths were grouped according to Wigglesworth's classification: 20% were due to prematurity and 24% to birth asphyxia. These two categories contributed to almost half of the perinatal deaths. Classification of perinatal deaths using Wigglesworth's classification appeared to be a practical and problem-oriented system. It also carried clear implications for improving perinatal care. The adoption of this method of classification by all major hospitals is recommended so that easy comparisons can be drawn over time and between different centres.

Asphyxia↗

Classification of perinatal death.

Three paediatric pathologists, one perinatal paediatrician, one obstetrician, and one epidemiologist separately used information collected on 239 babies in an attempt to validate the Wigglesworth classification of perinatal deaths. This was first done using clinical data only, then using the combination of clinical and gross necropsy findings and finally using clinical, gross necropsy, histological and any other information (for example, chromosome analyses, microbiological investigations). Only 14 (6%) of deaths changed groups within the Wigglesworth classification when gross necropsy findings were considered as well as clinical findings, and altogether only 21 (9%) changed classification when complete investigations were available. There was an unacceptable amount (15%) of disagreement between the classifiers, largely the result of failure to comply with the rules laid down for classification. We set out amendments to Wigglesworth's original definitions to clarify certain ambiguities.

Autopsy↗

Variability of undetermined manner of death classification in the US.

OBJECTIVES: To better understand variations in classification of deaths of undetermined intent among states in the National Violent Death Reporting System (NVDRS). DESIGN: Data from the NVDRS and the National Vital Statistics System were used to compare differences among states. MAIN OUTCOME MEASURES: Percentages of deaths assigned undetermined intent, rates of deaths of undetermined intent, rates of fatal poisonings broken down by cause of death, composition of poison types within the undetermined-intent classification. RESULTS: Three states within NVDRS (Maryland, Massachusetts, and Rhode Island) evidenced increased numbers of deaths of undetermined intent. These same states exhibited high rates of undetermined death and, more specifically, high rates of undetermined poisoning deaths. Further, these three states evidenced correspondingly lower rates of unintentional poisonings. The types of undetermined poisonings present in these states, but not present in other states, are typically the result of a combination of recreational drugs, alcohol, or prescription drugs. CONCLUSIONS: The differing classification among states of many poisoning deaths has implications for the analysis of undetermined deaths within the NVDRS and for the examination of possible/probable suicides contained within the undetermined- or accidental-intent classifications. The NVDRS does not collect information on unintentional poisonings, so in most states data are not collected on these possible/probable suicides. The authors believe this is an opportunity missed to understand the full range of self-harm deaths in the greater detail provided by the NVDRS system. They advocate a broader interpretation of suicide to include the full continuum of deaths resulting from self-harm.

Accidents↗

Expert review of non-Hodgkin's lymphomas in a population-based cancer registry: reliability of diagnosis and subtype classifications.

Incidence rates of non-Hodgkin's lymphomas (NHLs) have nearly doubled in recent decades. Understanding the reasons behind these trends will require detailed surveillance and epidemiological study of NHL subtypes in large populations, using cancer registry or other multicenter data. However, little is known regarding the reliability of NHL diagnosis and subtype classification in such data, despite implications for the accuracy of incidence statistics and studies. Expert pathological re-review was completed for 1526 NHL patients who were reported to the Greater Bay Area Cancer Registry and who participated in a large population-based case-control study. Agreement of registry diagnosis with expert diagnosis and with International Classification of Diseases for Oncology-2 (Working Formulation) subtype classifications was measured with positive predictive values and kappa statistics. Agreement of registry and expert diagnoses was high (98%). Thirty patients were found on review not to have NHL; most of these had leukemia. For subtypes, agreement of registry and expert classification was more moderate (59%). Agreement varied substantially by subtype from 5% to 100% and was 77% for the most common subtype, diffuse large cell lymphoma. Seventy-seven percent of 128 registry-unclassified lymphomas were assigned a subtype on re-review. Our analyses suggest excellent diagnostic reliability but poorer subtype reliability of NHL in cancer registry data information that is critical to the interpretation of lymphoma time trends. Thus, overall NHL incidence and survival statistics from the early 1990s are probably accurate, but subtype-specific statistics could be substantially biased, especially because of high (15-20%) proportions of unclassified lymphomas.

Adult↗

The classification of anxiety disorders in ICD-10 and DSM-IV: a concordance analysis.

On the surface, the classifications of anxiety disorders in DSM-IV and ICD-10 appear quite similar. However, differences exist and are evident in four aspects of the diagnostic criteria: typology, identifying criteria, inclusion and exclusion criteria. The current study uses data from the Australian National Mental Health Survey to model the impact of these differences on the diagnosis of generalized anxiety disorder. The results show that the concordance between the current classifications would be improved with the removal of the criterion for uncontrollability from DSM-IV, a closer focus on the symptoms of hypervigilance and scanning as in DSM-IV and the removal of the clinical significance criterion from DSM-IV. Equivalency of the exclusion criteria between the two classification systems reduces the concordance, demonstrating that each classification systems is a set of interdependent diagnoses, and to ultimately achieve concordance, all diagnoses must be considered together.

Anxiety Disorders↗

Classification systems for psychiatric diseases currently used in Japan.

A research survey was conducted to find out which diagnostic classification systems were commonly used in university hospitals in Japan. By using a questionnaire, we collected data to determine which diagnostic classification systems and diagnostic criteria were being used to identify schizophrenia, affective (mood) disorders, and neurosis. The results indicated that most university hospitals used either the International Classification of Diseases (ICD) of the WHO or the Diagnostic and Statistical Manual of Mental Disorders (DSM) of the American Psychiatric Association. For research of academic presentations, more than 60% of the surveyed institutions used DSM. In clinical settings, however, ICD and DSM were used with similar frequencies (42-43%). It was also noted that 15% of institutions still use a traditional psychiatric diagnosis for schizophrenia and mood disorders. This paper addresses historical aspects of the diagnosis and classification of mental illness in Japan.

Cross-Cultural Comparison↗

Accuracy-based learning classifier systems: models, analysis and applications to classification tasks.

Recently, Learning Classifier Systems (LCS) and particularly XCS have arisen as promising methods for classification tasks and data mining. This paper investigates two models of accuracy-based learning classifier systems on different types of classification problems. Departing from XCS, we analyze the evolution of a complete action map as a knowledge representation. We propose an alternative, UCS, which evolves a best action map more efficiently. We also investigate how the fitness pressure guides the search towards accurate classifiers. While XCS bases fitness on a reinforcement learning scheme, UCS defines fitness from a supervised learning scheme. We find significant differences in how the fitness pressure leads towards accuracy, and suggest the use of a supervised approach specially for multi-class problems and problems with unbalanced classes. We also investigate the complexity factors which arise in each type of accuracy-based LCS. We provide a model on the learning complexity of LCS which is based on the representative examples given to the system. The results and observations are also extended to a set of real world classification problems, where accuracy-based LCS are shown to perform competitively with respect to other learning algorithms. The work presents an extended analysis of accuracy-based LCS, gives insight into the understanding of the LCS dynamics, and suggests open issues for further improvement of LCS on classification tasks.

Algorithms↗

Spatial profiles of local and nonlocal effects upon contrast detection/discrimination from classification images.

We used classification images (A. J. Ahumada, Jr., & J. Lovell, 1971) to estimate the perceptual filter in a task designed to assess both local and nonlocal effects upon contrast detection/discrimination. Three observers performed a yes/no detection or discrimination task of a uniform circular decrement (radius = 0.68 deg) near threshold presented for 100 to 400 ms. Stimuli were presented in ring image noise that either covered the signal and an annular surrounding area (out to 1.36 deg), or only the surrounding annular area (out to 1.36 deg). Both the signal and the annular surround appeared on a uniform background. With ring noise over both the signal and surround, the amplitudes of the classification images in the signal area decreased as radial distance increased from the signal/surround border, and no effect of the surround was found. With ring noise only in the surround, classification images indicated noncontiguous effects at both the signal/surround border (local) and the surround/background border (nonlocal). The spatial extents of the nonlocal effects (< 0.07 deg) were smaller than local effects (0.25 deg), whereas the peak amplitudes of the local and nonlocal effects were comparable. These results suggest that the nonlocal effects were smaller than the local effects, and that the smaller effects would be due to smaller effective areas, as opposed to smaller amplitudes over the same area. Little or no change was found in the classification images across stimulus duration, suggesting that both the local and nonlocal processes found in this study were completed within 100 ms.

Adult↗

Looking for an order of things: textbooks and chemical classifications in nineteenth century France.

The purpose of this paper is to reconsider the issue of the creativity of textbook writing by exploring the links between nineteenth-century French textbooks and the quest for a classification of elements. The first section presents the elegant combination of didactic and chemical constraints invented by eighteenth-century chemists: the order of learning - from the known to the unknown - and the order of things - from the simple to the complex - were one and the same. In section two we argue that the alleged coincidence did not help the authors of elementary textbooks required for the new schools set up by the French revolution. Hence the variety of classifications adopted in the early nineteenth century. A debate between natural and artificial classifications raised a tension in the 1830s without really dividing the chemical community. Rather it ended up with the adoption of a hybrid classification, combining the rival natural and artificial systems.

Chemistry↗

MWENA: a novel sample re-weighting-based algorithm for disease classification and data interpretation using extracellular vesicles omics data.

BACKGROUND AND OBJECTIVE: Extracellular vesicles (EVs), considered as a form of liquid biopsy, have gained significant attention in recent years due to their stability and the preservation of disease markers. Research studies underscore the clinical significance of molecules found in EVs, highlighting their role as communicative mediators between cells. However, analyzing this data is challenging due to noisy measurements, having far more variables than samples, and some groups (e.g., disease subtypes or experimental conditions) having much less data than others. We therefore develop an algorithm to address aforementioned challenges for the classification of imbalanced EVs omics data. METHODS AND RESULTS: We propose the EV Meta-Weight Elastic Net Algorithm (MWENA), which utilizes logistic regression with elastic net regularization for the classification and identification of EV signatures, effectively addressing the challenges posed by high-dimensional small sample sizes. To mitigate issues related to class imbalance and high noise levels, MWENA incorporates an automatic sample re-weighting function, which uses a meta-net to adaptively learn generalizable patterns directly from the data itself. We validate the MWENA algorithm on both simulated data and EVs omics data, covering six classification tasks that involve four different types of diseases (pancreatic ductal adenocarcinoma, interstitial lung diseases, colorectal cancer, and ovarian cancer) and three clinical scenarios (disease diagnosis, disease-stage screening, and disease-subtype classification). Compared to other machine learning methods, MWENA demonstrates superiority in identifying small class samples and achieves the highest scores in both sensitivity and G-means. Biological analysis is also performed to further explore the significance of selected signatures as biological markers and their roles in disease mechanisms. CONCLUSIONS: We anticipate that our proposed approach will take a modest step in harnessing EV omics data to discover biomarkers, aiding researchers in gaining a comprehensive understanding of biological processes.

Extracellular Vesicles↗

Classification of primary headaches.

Given the range of disorders that produce headache, a systematic approach to classification and diagnosis is an essential prelude to clinical management. For the last 15 years, the diagnostic criteria of the International Headache Society (IHS) have been the accepted standard. The second edition of The International Classification of Headache Disorders (January 2004) reflects our improved understanding of some disorders and the identification of new disorders. Neurologists who treat headache should become familiar with the revised criteria. Like its predecessor, the second edition of the IHS classification separates headache into primary and secondary disorders. The four categories of primary headaches include migraine, tension-type headache, cluster headache and other trigeminal autonomic cephalalgias, and other primary headaches. There are eight categories of secondary headache. Important changes in the second edition include a restructuring of these criteria for migraine, a new subclassification of tension-type headache, introduction of the concept of trigeminal autonomic cephalalgias, and addition of previously unclassified primary headaches. Several disorders were eliminated or reclassified. In this article, the authors present an overview of the revised IHS classification, highlighting the primary headache disorders and their diagnostic criteria. They conclude by presenting an approach to headache diagnosis based upon these criteria.

Adult↗

Anatomic/radiologic classification of renal cortical nodules.

A cortical nodule is defined as a focus of more normal renal cortical tissue than usual in an otherwise normal kidney. Such nodules can mimic renal tumors on excretory urography. The urographic features of 29 cases of renal cortical nodules were correlated with arteriographic features to determine if a pattern of different anatomic types could be discerned. The cortical nodules fell into three anatomic groups (similar to Hodson's dissection classification): (1) subcapsular, (2) hilar lip, and (3) septa of Bertin. These anatomic groupings are proposed as a classification scheme for radiologic use. Using this classification, it was found that cortical nodules have urographic features that permit distinction of one type of cortical nodule from another. A better understanding of cortical nodules, based on this classification, should result in more certain urographic diagnosis of cortical nodule. Similarly, mistaking a cortical nodule for renal tumor on urography should be less likely to occur.

Adolescent↗

Classification systems in orthopaedics.

Classification systems help orthopaedic surgeons characterize a problem, suggest a potential prognosis, and offer guidance in determining the optimal treatment method for a particular condition. Classification systems also play a key role in the reporting of clinical and epidemiologic data, allowing uniform comparison and documentation of like conditions. A useful classification system is reliable and valid. Although the measurement of validity is often difficult and sometimes impractical, reliability-as summarized by intraobserver and interobserver reliability-is easy to measure and should serve as a minimum standard for validation. Reliability is measured by the kappa value, which distinguishes true agreement of various observations from agreement due to chance alone. Some commonly used classifications of musculoskeletal conditions have not proved to be reliable when critically evaluated.

Classification↗

Using classification tree and logistic regression methods to diagnose myocardial infarction.

Early and accurate diagnosis of myocardial infarction (MI) in patients who present to the Emergency Room (ER) complaining of chest pain is an important problem in emergency medicine. A number of decision aids have been developed to assist with this problem but have not achieved general use. Machine learning techniques, including classification tree and logistic regression (LR) methods, have the potential to create simple but accurate decision aids. Both a classification tree (FT Tree) and an LR model (FT LR) have been developed to predict the probability that a patient with chest pain is having an MI based solely upon data available at time of presentation to the ER. Training data came from a data set collected in Edinburgh, Scotland. Each model was then tested on a separate Edinburgh data set, as well as on a data set from a different hospital in Sheffield, England. Previously published models, the Goldman classification tree[1] and Kennedy LR equation[2], were evaluated on the same test data sets. On the Edinburgh test set, results showed that the FT Tree, FT LR, and Kennedy LR performed equally well, with ROC curve areas of 94.04%, 94.28%, and 94.30%, respectively, while the Goldman Tree's performance was significantly poorer, with an area of 84.03%. The difference in ROC areas between the first three models and the Goldman model is significant beyond the 0.0001 level. On the Sheffield test set, results showed that the FT Tree, FT LR, and Kennedy LR ROC areas were not significantly different (p > = 0.17), while the FT Tree again outperformed the Goldman Tree (p = 0.006). Unlike previous work[3], this study indicates that classification trees, which have certain advantages over LR models, may perform as well as LR models in the diagnosis of patients with MI.

Algorithms↗