Has De-listing Routine Eye Examinations Increased the Use of Primary Care Providers for Ocular Diagnoses?
Bibliographic record
Abstract
Purpose: In 2004, Ontario de-listed routine eye examinations for individuals aged 20-64. Post-delisting, affected individuals pay out-of-pocket or have the costs covered by private insurance for eye care. To avoid payments, patients with eye problems may visit a government-insured primary care provider (PCP) for help and/or for a referral to see a government-insured optometrist/ophthalmologist. We investigated if PCP utilization for eye diagnoses increased among affected Ontarians post- versus pre-delisting. Methods: PCP utilization for eye diagnoses from 2000-2014 was assessed using administrative data and interrupted time series analysis. Eye diagnoses were identified using ICD-9 diagnostic codes. Results: Post-delisting, PCP utilization for eye diagnoses significantly increased among affected Ontarians (20-39 group: 30%, p
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".