Association of Intrauterine Growth with Retinopathy of Prematurity Risk in Very Preterm Infants: A Multicenter Cohort Study
Bibliographic record
Abstract
INTRODUCTION: The impact of intrauterine growth status as measured by BW percentiles on retinopathy of prematurity (ROP) pathogenesis remains inadequately characterized. The objectives of the study were to establish BW percentile-specific risk gradients for ROP development. METHODS: A multicenter cohort study was conducted with data were collected from Chinese Neonatal Network between January, 2019 and December, 2021. The exposure was GA- and sex-specific BW percentile. The primary outcome was incidence of ROP. Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated, adjusted for potential confounders, and stratified by GA, infant sex, maternal hypertension, singleton/multiple birth. RESULTS: Totally 17,882 preterm infants were enrolled, BW was 1,300.0 (1,100.0-1,500.0) g and GA was 29.9 (28.6-31.0) weeks. The incidence was 27% for any stage ROP and 3.7% for severe ROP (stage 3 or above). Each decrease of BW percentile by 10% was associated with 15% increase of odds for either any stage ROP (aOR 0.85 [95% CI: 0.83-0.86]) or severe ROP (aOR 0.85 [95% CI: 0.82-0.89]). The optimal discriminative BW percentile on receiver operating characteristic curve was 26% for predicting any stage ROP and 19% for predicting severe ROP. Lower BW percentile under these cut-offs were associated with elevated odds of any stage ROP (aOR 2.20 [95% CI: 1.97-2.47] and severe ROP (aOR 2.91 [95% CI: 2.22-3.80]). CONCLUSIONS: ROP incidence was negatively associated with BW percentile. Each 10% decreased BW percentile was associated with 15% increased odds of any stage ROP and severe ROP.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".