Is There an Urban-Rural Divide? Population Surveys of Rheumatic Musculoskeletal Disorders in the Pune Region of India Using the COPCORD Bhigwan Model
Bibliographic record
Abstract
OBJECTIVE: To estimate urban prevalence of rheumatic musculoskeletal (MSK) disorders and compare to an earlier rural regional study. METHODS: We screened 8145 adults from a preselected urban locality in Pune, India, for MSK pain in a cross-sectional house-to-house survey (Stage I) over 20 weeks. The World Health Organization-International League of Associations for Rheumatology (WHO-ILAR) Community Oriented Program for Control of Rheumatic Diseases (COPCORD) Bhigwan model was used. Thirty trained community volunteers completed Phases I and II questionnaires, concurrent with rheumatology evaluation (Phase III). Clinical diagnosis was based on standard diagnosis/classification criteria. Point prevalence rates from our survey and the earlier Bhigwan village (Pune district) survey were standardized (adjusted age-sex to India population census 2001) and are reported for osteoarthritis (OA), rheumatoid arthritis (RA), seronegative spondyloarthritis (SSA), and inflammatory arthritis (IA). RESULTS: One thousand one hundred fifty-two urban cases (65% women) were identified (14.1%, 95% confidence interval 13.4, 14.9). The self-reported pain sites (Phase II) were hip (0.4), knees (6.3), ankle (1.9), feet (0.7), shoulders (2), hands (1.3), wrist (1.2), neck (1.9), upper back (1.7), low back (5.5), thigh (1.5), calf (1.4), and sole (0.8); corresponding rural sites being hip (1.1), knees (13.7), ankle (7), feet (1.6), shoulders (7.9), hands (6.3), wrist (6.9), neck (6.8), upper back (8.4), low back (12.6), thigh (4.8), calf (7.1) and sole (2.2). OA disorders, soft tissue rheumatism (STR) and ill-defined aches and pains were predominant in both surveys; < 10% reported IA. The major disorders among urban cases were OA (4), STR (1.2), RA (0.2, ACR criteria 1988), undifferentiated IA (0.3), SSA (0.3), and gout (0.06); corresponding rates in Bhigwan were OA (6.3), STR (3.8), RA (0.5), undifferentiated IA (0.8), SSA (0.3), and gout (0.1). Infections were conspicuously absent. CONCLUSION: While similar in spectrum, standardized prevalence rates of self-reported pain sites and rheumatic MSK disorders were significantly lower in the urban (current Pune COPCORD surveys) versus rural (Bhigwan) community, and in both communities aches and pains that are poorly understood by modern science were predominant.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".