Polymyalgia rheumatica prevalence in a population‐based sample
Bibliographic record
Abstract
OBJECTIVE: To determine polymyalgia rheumatica (PMR) prevalence using population-based administrative data, and to estimate the error associated with case ascertainment approaches when using these databases. METHODS: Cases were ascertained using physician billing and hospitalization data from the province of Manitoba (population 1.1 million). Focusing on the population age >/=45 years, we compared 3 different case definition algorithms and also used statistical methods that accounted for imperfect case ascertainment to estimate the prevalence and the properties of the ascertainment algorithms. A hierarchical Bayesian latent class regression model was developed that also allowed us to assess differences across patient demographics (sex and region of residence). RESULTS: Using methods that account for the imperfect nature of both billing and hospitalization databases, we estimated the prevalence of PMR in women age >/=45 years to be lower in urban areas (754.5 cases/100,000; 95% credible interval [95% CrI] 674.1-850.3) compared with rural areas (1,004 cases/100,000; 95% CrI 886.3-1,143). This regional trend was also seen in men age >/=45 years, where the prevalence was estimated at 273.6 cases/100,000 (95% CrI 219.8-347.6) in urban areas and 380.7 cases/100,000 (95% CrI 311.3-468.1) in rural areas. Billing data appeared more sensitive in ascertaining cases than hospitalization data, and a large proportion of diagnoses was made by physicians other than rheumatologists. CONCLUSION: These data suggest a higher prevalence of PMR in rural versus urban regions. Our approach demonstrates the usefulness of methods that adjust for the imperfect nature of multiple information sources, which also allow for estimation of the sensitivity of different case ascertainment approaches.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".