Prevalence of vernal keratoconjunctivitis in Canada: a cross-sectional survey study
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of vernal keratoconjunctivitis (VKC) in Canada in January 2023. METHODS: A total of 1 061 ophthalmologists registered with the Canadian Ophthalmological Society were contacted in January 2023 and offered an online survey on VKC. Eighty (7.5%) responded. The prevalence of VKC in Canada was estimated using 6 distinct hypotheses, each based on different assumptions regarding disease duration and the potential experiences of nonresponding ophthalmologists. RESULTS: On the basis of the 6 different hypotheses, the estimated prevalence (per 10,000) of active VKC in January 2023 ranged between 0.13 (95% confidence interval [CI] 0.12-0.14) to 3.34 (95% CI 3.29-3.40) for the total population of Canada, and 0.61 (95% CI 0.56-0.66) to 16.11 (95% CI 15.84-16.39) for Canadians aged 0-19 years. Building on the most likely hypothesis that the disease duration was 4 years, and assuming on-responding ophthalmologists had seen half of the VKC cases as survey responders, the best estimate of VKC prevalence in Canada was 0.90 (95% CI 0.84-0.96) per 10,000 population and 4.33 (95% CI 4.07-4.61) per 10,000 children and teens. The most common VKC-associated complications were reported to be shield ulcer and steroid-induced ocular hypertension ranging from 0.02 to 0.48 and 0.02 to 0.47 cases per 10,000 inhabitants, respectively, depending on the hypothesis assumed. CONCLUSIONS: This national survey reported that the prevalence of VKC per 10,000 population in 2023 ranged from 0.13 to 3.34 for Canadians of all ages and from 0.61 to 16.11 for children and teens. The best estimate was 0.90 per 10,000 population of all ages and 4.33 per 10,000 children and teens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".