Validity of the COPCORD Core Questionnaire as a Classification Tool for Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: Rheumatic diseases are vastly underdiagnosed and undertreated, particularly among minorities and those of low socioeconomic status. The WHO-ILAR Community Oriented Program in the Rheumatic Diseases (COPCORD) advocates screening of musculoskeletal complaints in the community. The objective of this study was to evaluate the performance of the COPCORD Core Questionnaire (CCQ) as a diagnostic tool for rheumatic diseases. METHODS: We conducted a cross-sectional study designed in parallel with a large COPCORD survey in Mexico. A subsample of 17,566 questionnaires, selected from 4 of the 5 states included in a national COPCORD survey were included in the analysis as a diagnostic test to evaluate sensitivity, specificity, receiver operating characteristics curve (ROC), and positive likelihood ratio (LR+) of the CCQ as a case-detection tool for rheumatic diagnosis and for the most frequent diagnoses identified in the survey, osteoarthritis, regional rheumatic pain syndromes, and rheumatoid arthritis (RA). Logistic regression with the questions with LR+ ≥ 1 was performed to identify the strength of association (OR) for each question. RESULTS: Pain in the last 7 days, high pain score (> 4), and previous diagnosis were the questions with highest LR+ for diagnosis, and for diagnosis of RA treatment with NSAID. The variables that contributed most to the model were pain in the last 7 days (OR 2.0, 95% CI 1.8-2.3), NSAID treatment (OR 3.3, 95% CI 3.0-3.7), a high pain score (OR 1.15, 95% CI 1.13-1.17), and having a previous diagnosis (OR 1.4, 95% CI 1.3-1.6). These 4 questions had R(2) = 0.24, p < 0.01, for detection of any rheumatic diagnosis. The single variable that explains 16% (OR 1.33, 95% CI 1.31-134) of variance was a high pain score in the last 7 days. CONCLUSION: Some variables were identified in the CCQ that could be combined in a brief version for case detection of rheumatic diseases in community surveys. The validity of this proposal has to be tested against the original version.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".