Prevalence and factors associated with musculoskeletal disorders and rheumatic diseases in indigenous Maya-Yucateco people: a cross-sectional community-based study
Bibliographic record
Abstract
This study aimed to estimate the prevalence of musculoskeletal disorders and rheumatic diseases in indigenous Maya-Yucateco communities using Community-Oriented Program for Control of Rheumatic Diseases (COPCORD) methodology. The study population comprised subjects aged ≥18 years from 11 communities in the municipality of Chankom, Yucatan. An analytical cross-sectional study was performed, and a census was used. Subjects positive for musculoskeletal (MSK) pain were examined by trained physicians. A total of 1523 community members were interviewed. The mean age was 45.2 years (standard deviation (SD) 17.9), and 917 (60.2 %) were women. Overall, 592 individuals (38.8 %; 95 % CI 36.3-41.3 %) had experienced MSK pain in the last 7 days. The pain intensity was reported as "strong" to "severe" in 43.4 %. The diagnoses were rheumatic regional pain syndromes in 165 (10.8 %; 95 % CI 9.4-12.5), low back pain in 153 (10.0 %; 95 % CI 8.5-11.6), osteoarthritis in 144 (9.4 %; 95 % CI 8.0-11.0), fibromyalgia in 35 (2.2 %; 95 % CI 1.6-3.1), rheumatoid arthritis in 17 (1.1 %; 95 % CI 0.6-1.7), undifferentiated arthritis in 8 (0.5 %; 95 % CI 0.2-0.8), and gout in 1 (0.06 %; 95 % CI 0.001-0.3). Older age, being female, disability, and physically demanding work were associated with a greater likelihood of having a rheumatic disease. In conclusion, MSK pain and rheumatic diseases were highly prevalent. The high impact of rheumatic diseases on daily activities in this indigenous population suggests the need to organize culturally-sensitive community interventions for the prevention of disabilities caused by MSK disorders and diseases.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".