Prevalence of Musculoskeletal Pain and Rheumatic Diseases in the Southeastern Region of Mexico. A COPCORD-Based Community Survey
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of musculoskeletal (MSK) pain and rheumatic diseases in the southeastern Mexican state of Yucatán. METHODS: Using the Community Oriented Program in the Rheumatic Diseases (COPCORD) methodology, we performed a door-to-door, cross-sectional study generated through a multistage, stratified, randomized method on 3915 adult residents (age 42.7 ± 17.1 yrs; women 61.8%; urban setting 45.7%) of the Mexican state of Yucatán. We used universally accepted criteria for the diagnosis or classification of rheumatoid arthritis (RA), osteoarthritis (OA; knee and hand), fibromyalgia, systemic lupus erythematosus (SLE), gout, ankylosing spondylitis, regional rheumatic pain syndromes, and inflammatory back pain. RESULTS: Nontraumatic MSK pain in the last 7 days was present in 766 (19.6%; 95% CI 18.3-20.8) individuals. MSK pain was more prevalent in women (26.6%) versus men (12.2%; p < 0.01). Self-reported MSK disability occurred in 1.7%. Most MSK pain-related variables were consistently more prevalent in the urban setting. The prevalence of rheumatic disease was: OA 6.8% (95% CI 6.0-7.6); back pain 3.8% (95% CI 3.2-4.4); RA 2.8% (95% CI 2.2-3.3); rheumatic regional pain syndromes 2.3% (95% CI 1.9-2.8); inflammatory back pain 0.7% (95% CI 0.5-1.0); fibromyalgia 0.2% (95% CI 0.1-0.4); gout 0.1% (95% CI 0.07-0.3); and SLE 0.07% (95% CI 0.01-0.2). CONCLUSION: The prevalence of MSK pain was 19.6%. MSK pain was more prevalent in women and in the urban setting. A remarkably high prevalence of RA was found in this population, which suggests a role for geographic factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".