Prevalence of glaucoma in Canada: results from the 2016–2019 Canadian Health Measures Survey
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of glaucoma in Canada based on self-reports and test data, including Frequency Doubling Technology Perimetry (FDT), optic nerve vertical cup-to-disc ratio (CDR), intraocular pressure (IOP), and use of glaucoma medications. DESIGN: Cross-sectional survey. PARTICIPANTS: 2,600-4,100 participants aged 40-79 in the Canadian Health Measures Survey 2016-2019 with available information from self-report, CDR, FDT, and IOP. METHODS: Glaucoma was defined by self-reports, CDR ≥ 0.7 only, or failed FDT only. Incorporating results of CDR, FDT, IOP, and use of glaucoma medications, participants were further classified as definite glaucoma (failed FDT and CDR ≥ 0.7) or glaucoma suspects (CDR ≥ 0.7 only, failed FDT only, or IOP > 21 mmHg only, or "normal" values of FDT, CDR, and IOP but used glaucoma medications). Survey weights were used in analyses. RESULTS: The glaucoma prevalence was 2.5% (95% confidence interval [CI] 1.7%-3.3%) utilizing self-reports, 3.0% (95% CI 2.1%-3.9%) by CDR ≥ 0.7 only and 10.3% (7.8%-12.8%) with failed FDT only. Merging test data, the prevalence of definite glaucoma was 0.7% (95% CI 0.3%-1.1%) and the prevalence of suspected glaucoma was 16.3% (95% CI 13.2%-19.4%). Among the patients suspected of having glaucoma, 44.4% had ocular hypertension (OHT, mean IOP 22.8 mmHg) and 6.8% used glaucoma medications. IOP ≥28 mmHg was found in 2.4% of OHT individuals, and none used glaucoma medications.37.5% of Canadians with definite glaucoma were unaware they had glaucoma. CONCLUSIONS: Glaucoma prevalence in Canadians aged 40-79 varied between 0.7% and 10.3% depending on definition used. 16.3% of Canadians were labeled "glaucoma suspects". Nearly 40% of Canadians with definite glaucoma were unaware of having glaucoma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".