Refining evidence-based retinopathy of prematurity screening guidelines: The SCREENROP study
Bibliographic record
Abstract
PURPOSE: Retinopathy of prematurity (ROP) is a potentially blinding condition affecting premature infants for which less than 10% of babies undergoing screening require treatment. This study assessed and validated predictors of developing clinically significant ROP (type 2 or worse) and ROP requiring treatment. DESIGN: Nationwide retrospective cohort study. METHODS: This study included infants born between January 2014 and June 2016, admitted to level 3 neonatal intensive care units across Canada who underwent ROP screening. Data were derived from the Canadian Neonatal Network database. Predefined ≥ 1% risk for clinically significant retinopathy or prematurity and ROP requiring treatment was set as threshold for screening. Thirty-two potential predictors were analyzed, to identify and validate the most important ones for predicting clinically significant ROP. The predictors were determined on a derivation cohort and tested on a validation cohort. Multivariable logistic regression modeling was used for analysis. RESULTS: Using a sample of 4,888 babies and analyzing 32 potential predictors, capturing babies with ≥1% risk of developing clinically significant ROP equated to screening babies with birth weight (BW) <1,300 g or gestational age (GA) <30 weeks while capturing babies with ≥1% risk of requiring ROP treatment equated to screening babies with BW <1,200 g or GA <29 weeks. CONCLUSIONS: The Canadian ROP screening criteria can be modified to screen babies with BW <1,200 g or GA <30 weeks. Using these criteria, babies requiring treatment would be identified while reducing the number of babies screened unnecessarily.
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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 | 0.031 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".