Does patient education improve compliance to routine diabetic retinopathy screening?
Bibliographic record
Abstract
Introduction: Diabetic retinopathy (DR) screening relies on adherence to follow-up eye care. This article assesses if a model of patient education and tele-retina screening among high-risk patients with DR can achieve increased rates of compliance within a one-year follow-up. Methods: Between May 2014 and May 2016, DR screening was conducted in a cohort of 101 patients with diabetes in Southern Ontario. Optical coherence tomography and fundus photography images were used to visualize the retina remotely. Enrolled patients participated in an educational seminar at the screening site with the expressed purpose of enhancing patient understanding of DR. A chi-squared test was used to assess patient compliance to follow-up examinations within 6–12 months, while pre-to post-screening HbA1c levels were compared using a dependent t-test. Results: Of 101 patients who completed the study, 33 patients (32.6%) have never previously been screened for DR. Baseline compliance to annual screening increased from 36 patients (35.6%) to 51 patients (50.5%) after the tele-retina programme (p = 0.03). Eighty-nine patients (88%) were referred to an optometrist for ongoing care compared with 12 patients (11.9%) to an ophthalmologist for management of DR. Overall, 100 patients (99.0%) were satisfied with the tele-retina screening. There was no significant change in pre- to-post screening HbA1c levels (p = 0.91). Discussion: Patient education-focused tele-retina screening for DR significantly increased compliance to follow-up in a high-risk, non-compliant patient population. Management of diabetes as captured by HbA1c levels remain unchanged in the cohort indicating a need for ongoing inter-professional collaboration in education and vision screening.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".