Contrasting approaches to estimate the epidemiology of uveitis in Canadian health administrative data
Bibliographic record
Abstract
OBJECTIVE: This study aims to investigate the age- and sex-standardized incidence and prevalence of uveitis in Ontario, Canada, from 2000 to 2021. By employing various case definitions, this research seeks to discern trends in uveitis occurrence and provide a comprehensive understanding of its epidemiology. METHODS: A retrospective cohort study utilizing health administrative data was conducted. Multiple case definitions were employed to capture the diverse epidemiological trends of uveitis. Annual age/sex standardized incidence and prevalence rates with 95% confidence intervals (CI) were determined using annual population denominators. RESULTS: The age- and sex-standardized incidence rates exhibited variations over the study period showing a general decline from 2000 to 2021, more notably in recent years. The case definition with one diagnosis code estimated an incidence per 100,000 people of 184.4 (95% CI: 181.5-187.2) in 2000 and 109.2 (95% CI: 107.4-110.9) in 2021. The standardized prevalence exhibited a consistent upward trend, with the case definition requiring "at least one diagnosis code ever" recording 1 998.3 (1 989.1-2 007.5) in 2000 and 2 761.2 (2 752.7-2 769.7) in 2021 per 100,000 people. Lower incidence and prevalence rates were observed when employing case definitions requiring more stringent criteria with additional uveitis-related health encounters. CONCLUSIONS: The estimated trends showed declining standardized incidence, but a persistent increase in prevalence rates over time. These insights contribute valuable knowledge for health care professionals, policymakers, and researchers on the rising prevalence of uveitis and implications for planning for appropriate health care provisions to meet growing demands for uveitis care.
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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 | 0.138 | 0.344 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".