Reasonable Classification Research Articles

Predictive slope stability early warning model based on CatBoost

A model for predicting slope stability is developed using Categorical Boosting (CatBoost), which incorporates 6 slope features to characterize the state of slope stability. The model is trained using a symmetric tree as the base model, utilizing ordered boosting to replace gradient estimation, which enhances prediction accuracy. Comparative models including Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Logistic Regression (LR) were introduced. Five performance evaluation metrics are utilized to assess the predictive capabilities of the CatBoost model. Based on CatBoost model, the predicted probability of slope instability is calculated, and the early warning model of slope instability is further established. The results suggest that the CatBoost model demonstrates a 6.25% disparity in accuracy between the training and testing sets, achieving a precision of 100% and an Area Under Curve (AUC) value of 0.95. This indicates a high level of predictive accuracy and robust ordering capabilities, effectively mitigating the problem of overfitting. The slope instability warning model offers reasonable classifications for warning levels, providing valuable insights for both research and practical applications in the prediction of slope stability and instability warning.

An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach.

To increase early identification and intervention of dyslexia, a prescreening instrument is critical to identifying children at risk. The present work sought to shorten and validate the 30-item Mandarin Dyslexia Screening Behavior Checklist for Primary School Students (the full checklist; Fan et al., , 19, 521-527, 2021). Our participants were 15,522 Mandarin-Chinese-speaking students and their parents, sampled from classrooms in grades 2-6 across regions in mainland China. A machine learning approach (lasso regression) was applied to shorten the full checklist (Fan et al., , 19, 521-527, 2021), constructing grade-specific brief checklists first, followed by a compilation of the common brief checklist based on the similarity across grade-specific checklists. All checklists (the full, grade-specific brief, and common brief versions) were validated and compared with data in our sample and an external sample (N = 114; Fan et al., , 19, 521-527, 2021). The results indicated that the six-item common brief checklist showed consistently high reliability (αs > .82) and reasonable classification performance (about 60% prediction accuracy and 70% sensitivity), comparable to that of the full checklist and all grade-specific brief checklists across our current sample and the external sample from Fan et al., , 19, 521-527, (2021). Our analysis showed that 2.42 (out of 5) was the cutoff score that helped classify children's reading status (children who scored higher than 2.42 might be considered at risk for dyslexia). Our final product is a valid, accessible, common brief checklist for prescreening primary school children at risk for Chinese dyslexia, which can be used across grades and regions in mainland China.

Efficient Intrusion Detection in Patient Health Records Based on LSTM-Based RNN

Advancements in Information and Communication Technology applications play an important role in the field of healthcare and bio-medical engineering. The process of image processing and analysis starts from receiving visual information to giving out the description of the scene for improving the visual appearance of the human viewer and extract the features of the data. Moreover, IDS can be deployed along with other security mechanisms as a line of defence to ensure security of system and network resources. There have been various efforts in designing IDS using Machine Learning (ML) techniques. Additionally, by developing a hybrid strategy for intrusion detection and classification and using feature engineering approaches to extract significant features for learning, among other things, efforts have been made to improve the classification performance of ML-based IDS. However, as networking technology have advanced, attack types have evolved as well. For this reason, an efficient and successful intrusion detection and classification system must be created. To address the issue and achieve good generalization ability for intrusion detection and classification, the paper presents empirical analysis of LSTM based RNN classifiers. The suggested methods' effectiveness is assessed using a range of performance metrics, such as recall, f-score, accuracy, precision, and False Positive Rate (FPR). The LSTM based RNN method recognize attacks with 99.2% accuracy with minimum time complexity (5 s).

Research on Classification Maintenance Strategy for More Electric Aircraft Actuation Systems Based on Importance Measure

In this paper, a practical maintenance algorithm is proposed to improve the reliability of actuation systems and their components, specifically addressing the consistency degradation caused by faults in the symmetric actuation system components of more electric aircraft (MEA). By integrating important measures with traditional genetic algorithms, the accuracy of the algorithm is improved. Prior to maintenance, a reasonable classification of components is built to mitigate the adverse effects of extreme fault conditions on the algorithm. This approach improves both the effectiveness and efficiency of the algorithm, rendering the overall maintenance strategy better suited for real-world needs. Finally, comparative simulations confirm the algorithm’s superior performance in reliability improvement, demonstrating its substantial contribution to the field of MEA maintenance and reliability.

Pathogenesis and treatment decisions of degenerative lumbar scoliosis

Degenerative lumbar scoliosis (DLS) is a spinal deformity characterized primarily by abnormal curvature of the lumbar vertebrae, commonly occurring in individuals aged 50 and above. Its pathogenesis involves various factors, including intervertebral disc degeneration, ligament and muscle degradation, as well as genetic and environmental elements. In terms of treatment, non-surgical treatments such as pharmacotherapy, physical therapy, and exercise are regarded as the primary approach. However, for severe cases, individualized surgical intervention is also a viable option, and reasonable classification is the key to surgical decision-making. Current research predominantly emphasizes the osseous structural aspects of the spine, neglecting neural and muscular factors. Future interdisciplinary research is expected to explore more comprehensive and effective treatment modalities, aiding in the restoration of spinal cord function in patients.

Analisis Penerapan 5S di Tempat Kerja: Pendekatan dari Perspektif Karyawan PT. PKC

A warehouse is a place used to store and store goods owned by a company. There are many activities carried out in the warehouse, starting from the goods data collection process to the goods management process. PT. PKC is not yet optimal and there are still several problems such as a poor management system and irregular spare parts delivery scheduling. Therefore, the 5S method is needed so that warehouse management can be better and more efficient. There is the 5S method (Seiri, Seiton, Seiso, Seiketsu & Shitsuke) which is a management approach to organizing, cleaning, and maintaining an efficient, orderly, and productive work environment. The results of evaluation of 5S implementation in PT warehouse. PKC The assessment of the implementation of Seiri is included in a good classification with a score of 62%, the assessment of the implementation of Seiton is also in a good classification with a score of 67%, the assessment for the implementation of Seiso, is classified as good with a score of 74%, while the assessment for Seiketsu is in a reasonable classification with a score of 50%, and the assessment for the implementation of Shitsuke is categorized as sufficient with a score of 50%. Keywords: Implementation, Warehouse, 5S Method

Geomechanical Characterization of a Brazilian Experimental Site: Testing, Interpretation, and Material Properties

Clarity in monitoring existing foundation structures demands innovative safety analysis methodologies for deep foundations, necessitating advanced models calibrated with real-world field parameters. Understanding controlled conditions, including geotechnical profiles, seismic attributes, and soil mechanics, is crucial. A dedicated research group at the University of São Paulo spent three years refining these conditions, characterizing an experimental field along a canal in São Paulo. This study pioneers geotechnical and geomechanical characterization of the region’s tertiary sediments in São Paulo, offering valuable insights for current and future applications. Standard penetration tests with torque (SPT-Torque), piezocone tests (CPTu), and measurement of wave velocity (Vs) with piezocone tests (S-CPTu) were carried out. The exploration of the subsoil shows that in up to 2 m of excavation, there are clays and silts, and in up to 25 m, there is a significant layer of compact fine sand that has high values of tip resistance and wave velocities more significant than 100 m/s. In the electric cone tests, the abacus used displayed a reasonable classification. All propositions identified the transition from the surface soil to the sandy soil at between 3 and 25 m. The soil classification values were obtained with the data of the field parameters, and the geotechnical and mechanical parameters were estimated. No differences were detected among the values found in the SPT-T and CPTu tests for the values obtained via cone resistance. This demonstrates the reliability of both methods. In addition, using the CPTu test to identify the stratigraphic profile horizons employing the soil’s mechanical behavior when the cone is driven proved appropriate.

Nutritional Quality Assessment of Miscellaneous Cassava Tubers Using Principal Component Analysis and Cluster Analysis.

Cassava is a staple crop in developing countries because its starchy roots provide essential dietary carbohydrates. The aim of this research was to conduct a comprehensive inquiry and scientific evaluation of the nutritional value of cassava tubers. Eight nutritional characteristics were examined in native and imported cassava variants: starch, reduced sugar, anthocyanins, protein, dietary fiber, quinic acid, vitamin C, and dry matter content. Principal component analysis (PCA) was conducted to minimize the dimensionality of the nutritional markers. A scientific assessment technique was developed to calculate a composite score for the various cassava samples. Analysis of the data revealed noticeable variance among the samples' nutritional indicators, suggesting varying degrees of association. Starch had a substantial positive link with lower sugar, protein, and dry matter content (p < 0.01). Anthocyanins and quinic acid interacted favorably (p < 0.05), and a positive link between protein and dry matter content was observed (p < 0.05); however, protein and dietary fiber interacted negatively (p < 0.05). The contribution rate of the top three PCA factors was over 76%, demonstrating that these factors incorporated the primary information acquired from the eight original nutritional indices, while maintaining excellent representativeness and impartiality. The experimental results showed a preliminary nutritional grade for 22 cassava tuber samples. The top five types were Guangxi Muci, Gui Cassava 4, Glutinous Rice Cassava, Huifeng 60, and Dongguan Hongwei. In the cluster analysis, the levels of similarity between the data showed that the 22 types of cassava tubers could be grouped into five categories, each with their own set of nutrients. This study promotes the directed breeding of cassava species and offers a theoretical foundation for creating and using various cassava varieties. Furthermore, this work lays the groundwork for a systematic and dependable technique for the quality assessment, comprehensive evaluation, and reasonable classification of cassava species and similar crops.

An evaluation of the validity of an aphasia friendly mood and anxiety measure for stroke patients

ABSTRACT Introduction People with post-stroke aphasia are at increased risk of experiencing depression and anxiety. Despite this, there is a lack of appropriate tools which can be used in routine stroke rehabilitation practice to support with the identification of patients experiencing these difficulties. The Stroke Aphasia Anxiety and Mood Screen (SAAMS) was developed by an interdisciplinary team of stroke rehabilitation professionals to improve mood and anxiety screening. The current study sought to evaluate the validity of this measure. Methods Twenty-one people undergoing in-patient stroke rehabilitation who experienced aphasia were administered the SAAMS, alongside the Stroke Aphasic Depression Questionnaire (SADQ-H10) and the Behavioural Outcomes of Anxiety (BOA). The inclusion criteria included patients whose aphasia prevented them from completing traditional self-report measures of mood and anxiety, but who demonstrated a reliable ability to correctly respond to yes/no closed questions in Speech and Language Therapy assessments. Results Both the SAAMS-anxiety (SAAMS-A) and depression (SAAMS-D) subscales achieved adequate internal consistency, and patients’ scores were correlated with observational measures of mood and anxiety. The SAAMS achieved fair classification accuracy for identifying clinical depression and anxiety. Conclusions Patients with aphasia provided reliable responses to questions about anxiety and depression, and this is of clinical importance. While the study identified that the SAAMS has reasonable classification accuracy, further research is required to validate the SAAMS more thoroughly before it can be used routinely in clinical practice.

Chinese Coastal Fishing Ports Classification Based on Remote Sensing Images

Fishing ports are important fishery production platforms, and the transformation of these has had a profound impact on the fishing industry. A reasonable classification solution is crucial for scientific understanding, development, and management of fishing ports. Current research on the use of spatial distributional characterization and the construction of a classification system for fishing ports to improve their management is limited. Therefore, in this study, a fishing port classification system was constructed using remote sensing images, with fishing port boundaries accurately extracted for classification. Using graphical summarization, fishing ports were classified into five types, including inland, estuarine, shoreline, gulf, and islands. A port type identification system was also constructed based on distance from the shoreline, water area, and circulation. Finally, fishing port characteristics and differences were investigated based on spatial superposition. The results demonstrate that shoreline and gulf types are the most prevalent fishing port types along the Chinese coast, accounting for 43% and 26% of the total number of fishing ports, respectively. This provides a strong foundation for China’s fishery production, processing, and trade. Through the establishment of a comprehensive scientific classification system, fishing port management can be modernized, supporting the sustainable development and utilization of coastal zones.

Autonomous surface crack identification for concrete structures based on the you only look once version 5 algorithm

Failure to repair roads in a timely manner may shorten their life and even cause traffic accidents. Thus, accurate crack detection and reasonable classification are crucial for road safety evaluation. In this study, an improved network model based on the You Only Look Once version 5 algorithm is presented, with three additional modules: The first module improves the data processing speed by replacing the C3 module in the original network with a lightweight network model. The second module was used to lighten network weight by reusing a simple convolution structure to equivalently represent the calculation of a convolution layer as a weighted sum of several small convolution blocks. And the third module is used to improve the detection accuracy by removing the upsampling and performing three-way splicing. The proposed model can detect different types of cracks, and an extensive ablation study is reported based on various combinations of the proposed modules. Based on training on a database of 5484 images, the results show that the improved network proposed in this study can effectively identify pavement cracks. Compared with the original network, mean Average Precision is increased by 5.98%, the inference time is reduced by 4.82%, and the model weight is decreased by 17.36%. Additionally, to comply with engineering practice, comparative experiments were conducted on the pre-rotated dataset. The results showed that compared with You Only Look Once version 8, the improved algorithm improved accuracy, recall, average accuracy, and F1 score by 3.28%, 8.46%, 3.79% and 5.89%, respectively. This study can serve as an important reference for the development of crack detection methods.

Taxonomy of Naumann’s and dusky, red-throated and black-throated thrush complexes (Aves: Turdidae) using morphological and molecular characteristics

Abstract Species are the basic units in evolution and biodiversity and thus a reasonable species classification is essential to assess and protect biodiversity. The classifications of the Naumann’s and dusky thrush complexes and the red-throated and black-throated thrush complexes are not well defined in Turdidae (Aves, Passeriformes). Herein, we use qualitative and quantitative morphological characters and molecular data to review the taxonomy of these thrush complexes. These four thrushes can be distinguished with qualitative features of feather color and striping. These birds cannot be divided by quantitative features, which only work on two groups of thrush complexes. Phylogenetic analysis based on mitochondrial genes indicate that all haplotypes might be divided into two monophyletic clades corresponding to two groups of thrush complexes. According to coalescence-based species delimitation and the gen-morph species concept, these two groups of thrush complexes should be separated as two distinctive species (T. naumanni and T. ruficollis) and including two subspecies per species. This species classification is also supported by gene flow analysis. There is a significant mixing between these two thrush species in the neighbor-joining (NJ) tree and the cluster analysis using a few microsatellite loci. This leads to a pattern of nuclear-mitochondrial discordance between the two birds that can be explained by several potential mechanisms. This study provides the first comprehensive classification of the two thrush complexes on a population level using morphological and molecular characteristics, which will be useful in expanding our understanding of species division among thrush complexes.

Class-Balanced Regularization for Long-Tailed Recognition

Long-tailed recognition performs poorly on minority classes. The extremely imbalanced distribution of classifier weight norms leads to a decision boundary biased toward majority classes. To address this issue, we propose Class-Balanced Regularization to balance the distribution of classifier weight norms so that the model can make more balanced and reasonable classification decisions. In detail, CBR separately adjusts the regularization factors based on L2 regularization to be correlated with the class sample frequency positively, rather than using a fixed regularization factor. CBR trains balanced classifiers by increasing the L2 norm penalty for majority classes and reducing the penalty for minority classes. Since CBR is mainly used for classification adjustment instead of feature extraction, we adopt a two-stage training algorithm. In the first stage, the network with the traditional empirical risk minimization is trained, and in the second stage, CBR for classifier adjustment is applied. To validate the effectiveness of CBR, we perform extensive experiments on CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets. The results demonstrate that CBR significantly improves performance by effectively balancing the distribution of classifier weight norms.

Multi-objective ecological restoration priority in China: Cost-benefit optimization in different ecological performance regimes based on planetary boundaries

In the context of the “United Nations Decade on Ecosystem Restoration”, optimizing spatiotemporal arrangements for ecological restoration is an important approach to enhancing overall socioecological benefits for sustainable development. However, against the background of ecological degradation caused by the human use of most natural resources at levels that have approached or exceeded the safe and sustainable boundaries of ecosystems, it is key to explain how to optimize ecological restoration by classified management and optimal total benefits. In response to these issues, we combined spatial heterogeneity and temporal dynamics at the national scale in China to construct five ecological performance regimes defined by indicators that use planetary boundaries and ecological pressures which served as the basis for prioritizing ecological restoration areas and implementing zoning control. By integrating habitat conservation, biodiversity, water supply, and restoration cost constraints, seven ecological restoration scenarios were simulated to optimize the spatial layout of ecological restoration projects (ERPs). The results indicated that the provinces with unsustainable freshwater use, climate change, and land use accounted for more than 25%, 66.7%, and 25%, respectively, of the total area. Only 30% of the provinces experienced a decrease in environmental pressure. Based on the ecological performance regimes, ERP sites spanning the past 20 years were identified, and more than 50% of the priority areas were clustered in regime areas with increased ecological stress. As the restoration area targets doubled (40%) from the baseline (20%), a multi-objective scenario presents a trade-off between expanded ERPs in areas with highly beneficial effects and minimal restoration costs. In conclusion, a reasonable classification and management regime is the basis for targeted restoration. Coordinating multiple objectives and costs in ecological restoration is the key to maximizing socio-ecological benefits. Our study offered new perspectives on systematic and sustainable planning for ecological restoration.

Random forest evaluation using multi-key homomorphic encryption and lookup tables

In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds).

Measuring Change over Time on Cognitive Screening Measures: An Evaluation of the Psychometric Properties of ACE-III, CBI-R, and EMQ for the Purpose of Dementia Screening

Introduction: Cognitive screening measures are an established part of a dementia assessment and often include measures of subjective difficulties, e.g., Everyday Memory Questionnaire (EMQ), informant-rated difficulty, Cambridge Behavioural Inventory – Revised (CBI-R), and objective cognitive assessments like Addenbrooke’s Cognitive Examination (ACE-III). While these measures have validity for the purpose of diagnosing dementia, in clinical practice they are often used outside of their evidence base for the purpose of cognitive re-assessment. The current study sought to evaluate the psychometric properties for the repeated use of these assessments. Methods: This study used a longitudinal design, which included 49 healthy controls, 33 people with subjective memory difficulties, and 10 people with Alzheimer’s disease (AD) being assessed twice, with approximately a 1-year interval between assessments. Results: The EMQ has adequate re-test reliability (r = 0.78), but people with an AD diagnosis rated their memory as better than those with SMD, making it unsuitable as a measure. The CBI-R had moderate re-test reliability (r = 0.62). However, deterioration on the CBI-R was not useful for diagnosing AD. The ACE-III has high re-test reliability (r = 0.89). A change of five was associated with reasonable classification accuracy for identifying AD and achieved statistical significance. Conclusion: Using a 1-year interval, of the three measures used in this study, only the ACE-III total score may be a useful measure of change over time, although it should be applied cautiously due to the lack of representativeness of the sample, and change scores should always be triangulated with other forms of evidence of deterioration.

Advances in the technology of surgical treatment for atlantoaxial dislocation

The etiology and pathological morphology of atlantoaxial dislocation (AAD) are complex. Based on the pathological anatomical characteristics of AAD, combined with clinical techniques, it is essential to formulate reasonable classification criteria and develop corresponding treatment strategies for different types of AAD. The satisfactory outcome of surgical treatment for AAD can only be achieved through the comprehensive application of various atlantoaxial reduction techniques, tension band releasing techniques, internal fixation and fusion techniques. This article discusses the latest advancements in surgical treatment techniques for AAD, thoroughly explores treatment strategies based on different types of AAD, and analyzes the practicality and effectiveness of clinical classification and treatment strategies. The posterior atlantoaxial facet releasing and distraction compression reduction technique may pose challenges to traditional treatment strategies in the future. In the development of surgical treatment techniques for AAD, microspinal surgical techniques may play a significant role in improving surgical methods and enhancing treatment outcomes.

Impact of Palliative Care Utilization among Kidney Cancer Patients in US Hospitals: A National-Scale Analysis

The objective of this study was to examine the characteristics and utilization patterns of palliative care among the kidney cancer patients using a large-scale representative population-based sample. We retrospectively analyzed National Inpatient Sample hospitalization data from January 2020 to December 2020 to explore disparities in delivering palliative care to deceased kidney cancer patients and assess its impact on healthcare usage, focusing on hospital costs and length of stay (LoS). We used ICD-10 CM codes (International Classification of Diseases-classifying diagnoses and reasons for visits in all healthcare settings) to categorize the study population. We conducted univariable and multivariable linear and logistic regression analyses to calculate coefficients and odds ratios, considering relevant variables and addressing potential confounding factors. We studied 1437 deceased kidney cancer patients, with 53.9% receiving palliative care. Those receiving palliative care had lower total costs ($99,804 vs. $1,34,731) and a shorter LoS (7.19 days vs. 7.66 days), compared to those who didn’t. No significant difference was discovered in LoS. Private insurance, higher income, and female patients were more likely to receive palliative care. Race, hospital teaching status, and hospital size showed no significant differences. This study revealed insights into palliative care use among deceased kidney cancer patients, with cost-saving benefits evident. Disparities showed that individuals with private insurance and higher income more likely accessed palliative care, and females received it more often than males. Physicians and policymakers must consider these findings for equitable resource allocation and improved access.

Automated facial expression measurement in a longitudinal sample of 4- and 8-month-olds: Baby FaceReader 9 and manual coding of affective expressions

Facial expressions are among the earliest behaviors infants use to express emotional states, and are crucial to preverbal social interaction. Manual coding of infant facial expressions, however, is laborious and poses limitations to replicability. Recent developments in computer vision have advanced automated facial expression analyses in adults, providing reproducible results at lower time investment. Baby FaceReader 9 is commercially available software for automated measurement of infant facial expressions, but has received little validation. We compared Baby FaceReader 9 output to manual micro-coding of positive, negative, or neutral facial expressions in a longitudinal dataset of 58 infants at 4 and 8 months of age during naturalistic face-to-face interactions with the mother, father, and an unfamiliar adult. Baby FaceReader 9’s global emotional valence formula yielded reasonable classification accuracy (AUC = .81) for discriminating manually coded positive from negative/neutral facial expressions; however, the discrimination of negative from neutral facial expressions was not reliable (AUC = .58). Automatically detected a priori action unit (AU) configurations for distinguishing positive from negative facial expressions based on existing literature were also not reliable. A parsimonious approach using only automatically detected smiling (AU12) yielded good performance for discriminating positive from negative/neutral facial expressions (AUC = .86). Likewise, automatically detected brow lowering (AU3+AU4) reliably distinguished neutral from negative facial expressions (AUC = .79). These results provide initial support for the use of selected automatically detected individual facial actions to index positive and negative affect in young infants, but shed doubt on the accuracy of complex a priori formulas.

The fractured lens: a controversial revision of the International Classification of Primary Care.

The International Classification of Primary Care (ICPC) has represented the international standard reduction for measuring the content of primary care for over 30 years. In the process of its third revision, its authors, the Wonca International Classification Committee (WICC), delegated a major part of the technical work to a purposely formed Consortium. However, in the process of such revision, standard classification principles and rules have been inconsistently applied with the result that ICPC-3 has been published with major errors and an inconsistent structure. To formally describe and critically appraise the revision process of ICPC-3. The formal review of ICPC-3 performed by an expert group within WICC and commissioned by the Executive Council of Wonca Europe is presented in abridged form. ICPC-3 as currently presented introduces major departures from formal classification principles and rules, besides other major errors and inconsistencies, all of which are listed and described. Major changes in ICPC-3 defy categorisation and conceptualisation standards. ICPC-3 now represents an untested departure from international standard presentations, without a formal academic base. The direct inclusion of measures of functioning in a classification of reasons for encounter and health problems fails to address the dichotomy of these domains, the boundaries of and relationships between which are not satisfactorily resolved by the system. Analysis of ICPC-3 data will require the development and implementation of alternative, as yet undefined, models of the relationships between disease and health. By including different domains without resolving ambiguity, and by splitting function from other body systems, ICPC-3 becomes an internally fractured instrument.