Epidemiology of Rheumatic Diseases. A Community-Based Study in Urban and Rural Populations in the State of Nuevo Leon, Mexico
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of rheumatic diseases in rural and urban populations using the WHO-ILAR COPCORD questionnaire. METHODS: We conducted a cross-sectional home survey in subjects > 18 years of age in the Mexican state of Nuevo Leon. Results were validated locally against physical examination in positive cases according to an operational definition by 2 rheumatologists. We used a random, balanced, and stratified sample by region of representative subjects. RESULTS: We surveyed 4713 individuals with a mean age of 43.6 years (SD 17.3); 55.9% were women and 87.1% were from urban areas. Excluding trauma, 1278 individuals (27.1%, 95% CI 25.8%-28.4%) reported musculoskeletal pain in the last 7 days; the prevalence of this variable was almost twice as frequent in women (33% vs 17% in men); 529 (11.2%) had pain associated with trauma. The global prevalence of pain was 38.3%. Mean pain score was 2.4 (SD 3.4) on a pain scale of 0-10. Most subjects classified as positive according to case definition (99%) were evaluated by a rheumatologist. Main diagnoses were osteoarthritis in 17.3% (95% CI 16.2-18.4), back pain in 9.8% (95% CI 9.0-10.7), undifferentiated arthritis in 2.4% (95% CI 2.0-2.9), rheumatoid arthritis in 0.4% (95% CI 0.2-0.6), fibromyalgia in 0.8% (95% CI 0.6-1.1), and gout in 0.3% (95% CI 0.1-0.5). CONCLUSION: This is the first regional COPCORD study in Mexico performed with a systematic sampling, showing a high prevalence of pain. COPCORD is a useful tool for the early detection of rheumatic diseases as well as for accurately referring patients to different medical care centers and to reduce underreporting of rheumatic 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".