Epidemiology of the Rheumatic Diseases in Mexico. A Study of 5 Regions Based on the COPCORD Methodology
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of musculoskeletal (MSK) disorders and to describe predicting variables associated with rheumatic diseases in 5 regions of México. METHODS: This was a cross-sectional, community-based study performed in 5 regions in México. The methodology followed the guidelines proposed by the Community Oriented Program for the Control of the Rheumatic Diseases (COPCORD). A standardized methodology was used at all sites, with trained personnel following a common protocol of interviewing adult subjects in their household. A "positive case" was defined as an individual with nontraumatic MSK pain of > 1 on a visual analog pain scale (0 to 10) during the last 7 days. All positive cases were referred to internists or rheumatologists for further clinical evaluation, diagnosis, and proper treatment. RESULTS: The study included 19,213 individuals; 11,602 (68.8%) were female, and their mean age was 42.8 (SD 17.9) years. The prevalence of MSK pain was 25.5%, but significant variations (7.1% to 43.5%) across geographical regions occurred. The prevalence of osteoarthritis was 10.5%, back pain 5.8%, rheumatic regional pain syndromes 3.8%, rheumatoid arthritis 1.6%, fibromyalgia 0.7%, and gout 0.3%. The prevalence of MSK manifestations was associated with older age and female gender. CONCLUSION: The prevalence of MSK pain in our study was 25.5%. Geographic variations in the prevalence of MSK pain and specific diagnoses suggested a role for geographic factors in the prevalence of rheumatic diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".