The state of low vision care in Ontario
Bibliographic record
Abstract
Approximately half a million Canadians live with vision loss significantly impacting their quality of life. The prevalence of vision loss is expected to increase by 30% in the next decade as Canada’s senior patient population continues to rapidly increase.1 This demographic shift is producing a mounting vision loss epidemic and a strain on available resources. Although vision rehabilitation services are available for these patients, limited research has been done to study how these services by ophthalmologists are being provided. In this retrospective population-based study, we analyzed the patterns of provision and utilization of vision rehabilitation services in Ontario between 2009 and 2015. Billing data for low vision services were received from the Ontario Health Insurance Plan Database. Patient demographics (age, sex, geographic distribution, number and type of visits) and service provider information (geographic location, number of years providing services, and number of services per year) were analyzed. The majority of patients that received vision rehabilitation services were females (61%) and over 60 years old (79%). While patient and provider geographic distributions overlapped in the areas with the largest patient populations, many regions lacked services. Over the period analyzed, the majority of patients (71%) made only one vision rehabilitation visit and a small subset of patients (11%) made more than two visits. Nine providers practiced low vision for seven years, while 43 provided services for only one year. In 2015, the most common diagnostic service provided to low vision patients was Optical Coherence Tomography of the retina and the most common therapeutic service was intravitreal for wet age-related macular degeneration. Although low vision services increased between 2009 and 2015, we estimate that 5% or less of patients with low vision accessed these services. There were inequities in ability to access care based on age, sex, and geographic location. Our findings are expected to help inform future healthcare policy decisions, especially as considerations are made to provide for our ageing population. Importantly, there is a significant need to increase number of providers, service locations, and access for patients.\n1. CNIB - Fast Facts about Vision Loss. CNIB. http://www.cnib.ca/en/about/media/vision-loss/pages/default.aspx#canadians. Accessed August 26, 2017.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".