Bayesian estimation of the prevalence of osteoarthritis in the Québec elderly population from an administrative database
Bibliographic record
Abstract
Osteoarthritis (OA) is the most prevalent form of arthritis. It is a disabling condition that mostly affects the elderly with huge costs to society. Estimating the prevalence of OA is important for planning health services, for creating programs aimed to prevent OA disability and for assisting patients living with these disabilities. Several authors have attempted to estimate the prevalence of OA using data obtained from self-report questionnaires or administrative databases. Self-report questionnaires are prone to recall bias and estimates from administrative databases have relied solely on diagnostic codes, that can sometimes be inaccurate. If data from three presumed conditionally independent tests are used to estimate the prevalence, then the problem is identifiable, meaning that all parameters can be estimated without imposing constraints on the parameter space. When data from the three conditionally independent diagnostic tests were considered, the estimated prevalence of OA was 14.8% (95% CI: 14.5-15.1). A moderate degree of variation in this prevalence estimate was found across different models carrying different assumptions. The Bayesian latent class methodology used is advantageous in accounting for the different estimates that may arise from different sets of modelling assumptions. As the validity of the results rely on various assumptions, all of which are difficult to verify, final conclusions depend on which assumptions are thought most likely to be true, and the degree of robustness of estimates across different models.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".