Increased quantity and diversity of patient referrals following the introduction of a novel vision rehabilitation model
Bibliographic record
Abstract
Despite effective vision rehabilitation (VR) interventions, no gold standard model of care delivery has been established. The institution of the South East Ontario Vision Rehabilitation Service (SOVRS) introduced a centralized intake, an occupational therapist as a systems navigator, and improved communication pathways between low vision services in order to optimize regional VR care. The aim of this study is to compare the SOVRS model of VR to a traditional, hospital-based pre-SOVRS-implementation model using referral data. A single-site (Vision Rehabilitation Clinic at Kingston Health Sciences Center), retrospective medical chart review was performed. Data were gathered from the electronic medical records of patients who received a low vision assessment at the pre-SOVRS-implementation clinic (2017) and the SOVRS clinics (2019). A total of 245 charts were reviewed over the two study periods. There were no significant differences in the age, gender, or diagnoses causing vision loss between 2017 and 2019. One hundred nine incoming referrals were received in 2017, with 136 in 2019, representing a 25% increase in incoming referrals ( p < .001). The proportion of incoming referrals from non-ophthalmologists rose from 3.7% in 2017 to 31.9% in 2019 ( p < .001). The number of outgoing referrals also increased significantly, from 113 outgoing referrals in 2017 to 259 in 2019 ( p < .001), equivalent to a mean of 1.04 ± 0.68 (± standard deviation) outgoing referrals per incoming referral in 2017 and 1.90 ± 0.97 outgoing referrals per incoming referral in 2019. Outgoing service referrals also diversified significantly in 2019 ( p < .001), with more referrals to services such as VR health service organizations and community services. The SOVRS model was able to increase both the quantity and diversity of incoming and outgoing referrals by adopting several key strategies during its development. By expanding referrals, SOVRS increased the services available to patients and enabled a larger population to receive VR care.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".