Bibliographic record
Abstract
Purpose: To determine the current distribution of ophthalmologists across Ontario, and to determine the factors influencing cataract surgery waiting times in the province of Ontario. Study Design: Cross-sectional study. Methods: A list of all practicing ophthalmologists in Ontario and their main practice location were acquired from CPSO website. The total population count, breakdown by Local Health Integration Network (LHIN), and the population over 65 years of age were retrieved from Statistic Canadau2019s 2016 Census Profile online. Cataract surgery waiting times were obtained from Health Quality Ontario. Graphpad Prism 8 software was used for linear regression analysis.Results: There are currently 3.47 ophthalmologists per 100,000 total population in Ontario. However, there is a 5.5 fold range in ratios, from 1.63 in Central West to 9.01 in Toronto Central. As the population age 65 increases, there is a weak correlation to an increase in the number of ophthalmologists in the area (p = 0.04, R = 0.55). However, when considering the ophthalmologist-to-population 65+ ratios, this also varies significantly from 8.82 in North Simcoe Muskoka to 64.26 for Toronto Central, representing a 7.3 fold difference between the two ends of the spectrum. LHINs with a larger age 65+ population (p < 0.0001) and LHINs with more practicing ophthalmologists (p < 0.01) perform significantly more cataract surgeries per year. However, areas with a larger number of ophthalmologists did not have a shorter waiting time for cataract surgery (p = 0.37). Conclusions: At present, only 3 out of 14 LHINs meet a previously recommended ratio of 3.37 ophthalmologists per 100,000 population and this ratio varies greatly between LHINs. In addition, currently there is only a weak correlation between the distribution of ophthalmologists to age 65+ population. Although areas with more ophthalmologists were able to perform more cataract surgeries, having more ophthalmologists in a geographical area was not associated with a decrease in waiting time. As Ontario moves forward to transform the LHINs into Ontario Health, understanding the current state of Ontariou2019s ophthalmology workforce and challenges in ophthalmology service demand, including waiting times for cataract surgery, will allow for more appropriate allocation of resources and can improve access to healthcare for patients throughout the province.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".