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 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 | 0.002 | 0.000 |
| 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) | 0.000 | 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".