Rheumatoid arthritis prevalence in Quebec
Bibliographic record
Abstract
BACKGROUND: To estimate rheumatoid arthritis (RA) prevalence in Quebec using administrative health data, comparing across regions. METHODS: Cases of RA were ascertained from physician billing and hospitalization data, 1992-2008. We used three case definitions: 1) ≥ 2 billing diagnoses, submitted by any physician, ≥ 2 months apart, but within 2 years; 2) ≥ 1 diagnosis, by a rheumatologist; 3) ≥1 hospitalization diagnosis (all based on ICD-9 code 714, and ICD-10 code M05). We combined data across these three case definitions, using Bayesian hierarchical latent class models to estimate RA prevalence, adjusting for the imperfect sensitivity and specificity of the data. We compared urban versus rural regions. RESULTS: Using our case definitions and no adjustment for error, we defined 75,760 cases for an over-all RA prevalence of 9.9 per thousand residents. After adjusting for the imperfect sensitivity and specificity of our case definition algorithms, we estimated Quebec RA prevalence at 5.6 per 1000 females and 4.1 per 1000 males. The adjusted RA prevalence estimates for older females were the highest for any demographic group (9.9 cases per 1,000), and were similar in rural and urban regions. In younger males and females, and in older males, RA prevalence estimates were lower in rural versus urban areas. CONCLUSIONS: Without adjustment for error inherent in administrative databases, RA prevalence in Quebec was approximately 1%, while adjusted estimates are approximately half that. The lower prevalence in rural areas, seen for most demographic groups, may suggest either true regional variations in RA risk, or under-ascertainment of cases in rural Quebec.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".