G-ROP versus WINROP for retinopathy of prematurity screening: a Calgary perspective
Bibliographic record
Abstract
OBJECTIVE: Retinopathy of prematurity (ROP) remains one of the leading causes of childhood blindness. The current screening criteria in Canada have extremely high sensitivity but low specificity, leading to unnecessary examinations of neonates. Moreover, a screening algorithm that reduces the burden of screening is urgently needed owing to the increase in neonatal survival after extreme premature delivery, combined with the limited number of physicians qualified to screen for ROP. This retrospective study aimed to validate and compare the accuracy of the postnatal growth and ROP (G-ROP) and Weight, Insulin-like growth factor-1, Neonatal Retinopathy of Prematurity (WINROP) models for identifying neonates at risk for developing treatment-requiring ROP in a Canadian cohort. DESIGN: Single-center retrospective cohort study conducted in Calgary, Alberta, Canada. Data from preterm infants born between 23- and 31-week gestational age or birth weight less than or equal to 1 250 grams were analyzed. A total of 1 001 infants were included in the study. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value for WINROP, and G-ROP algorithms were assessed in identifying neonates at risk of treatment-requiring ROP. RESULTS: The WINROP algorithm yielded 95.7% sensitivity in identifying infants requiring ROP treatment compared to 100% sensitivity with the G-ROP model. Specificity for treatment-requiring ROP for WINROP was 41.7% and G-ROP was 30.4%. CONCLUSIONS: The G-ROP model was found to be more appropriate in our cohort, lending itself seamlessly to clinical care, while providing 100% sensitivity and greater specificity compared to current screening guidelines in our cohort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".