A randomised trial of non-mydriatic ultra-wide field retinal imaging versus usual care to screen for diabetic eye disease: rationale and protocol for the Clearsight trial
Bibliographic record
Abstract
INTRODUCTION: Suboptimal screening for diabetic eye disease is a major cause of preventable vision loss. Screening barriers include mydriasis and the extra time patients need to attend dedicated eye screening appointments. In the Clearsight trial, we are testing whether screening by non-mydriatic ultra-wide field (NM UWF) imaging on the day patients attend their diabetes outpatient clinic visit improves detection of clinically important eye disease compared with usual screening. METHODS AND ANALYSIS: Patients with diabetes due for a screening eye exam by the 2013 Canadian Diabetes Association (CDA) practice guidelines are being randomised to on-site screening by NM UWF imaging on the day of their clinic visit or to usual screening where, per CDA guidelines, they are encouraged to arrange an exam by an optometrist. The primary outcome is actionable eye disease (AED) based on a need for referral to ophthalmology and/or increased ocular surveillance. The primary analysis will use an intention-to-screen approach that compares the proportions of detected AED between on-site and usual screening groups under a superiority hypothesis in favour of on-site screening. With 740 randomised participants, the study will have 80% power to detect ≥5% absolute increase in the AED rate among on-site screening versus usual screening participants. This difference translates into a number-needed-to-screen by on-site screening of 20 to detect 1 additional person with AED. ETHICS AND DISSEMINATION: The protocol was approved by the institutional review board of Western University. The findings of the trial will be disseminated directly to participants and through peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: ClinicalTrials.Gov NCT02579837 (registered 16 October 2015). PROTOCOL ISSUE DATE: 18 November 2015.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".