Effectiveness of non-mydriatic ultra-widefield retinal imaging to screen for diabetic eye disease: a randomized controlled trial (Clearsight)
Bibliographic record
Abstract
Objective Suboptimal diabetic eye disease screening is a major cause of preventable vision loss. Screening barriers include mydriasis and need for dedicated screening appointments. The Clearsight trial assessed whether non-mydriatic ultra-widefield (NM UWF) screening on the day of a diabetes clinic visit improved detection of clinically important eye disease vs usual screening. Research Design and Methods This single-center, randomized, parallel-group controlled trial was conducted at St. Joseph’s Health Care, London, Canada. Adults with diabetes due for screening were randomized to same-day On-Site Screening (NM UWF imaging) on the day of a scheduled diabetes clinic visit or Usual Screening (encouraged to arrange optometrist screening). The primary outcome was detection of actionable eye disease (AED) defined as need for ophthalmology referral or increased ocular surveillance. The primary analysis (modified intention-to-screen) compared the proportions of AED between groups within one year of enrollment. Results Of 740 participants randomized between March 7, 2016 and April 17, 2019, 335 On-Site Screening and 323 Usual Screening participants met criteria for the primary analysis. More AED was detected in the On-Site Screening than Usual Screening groups (50/335 [14.9%] vs 22/323 [6.8%]; adjusted odds ratio 2.51 [95% CI 1.49–4.36]). The number-needed-to-screen by On-Site Screening to detect one additional patient with AED was 13 [95% CI 8–29]. Conclusions Same-day On-Site Screening by NM UWF imaging increased detection of clinically important diabetic eye disease vs Usual Screening. Integration of NM UWF imaging into routine diabetes clinic visits improved screening adherence and has the potential to prevent vision loss.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".