International variations and trends in the treatment for retinopathy of prematurity
Bibliographic record
Abstract
OBJECTIVE: To compare the rates of retinopathy of prematurity (ROP) and treatment of ROP by laser or intravitreal anti-vascular endothelial growth factor among preterm neonates from high-income countries participating in the International Network for Evaluating Outcomes (iNeo) of neonates. METHODS: weeks' gestation who were admitted to neonatal units in Australia/New Zealand, Canada, Finland, Israel, Japan, Spain, Sweden, Switzerland, Tuscany (Italy) and the UK between 2007 and 2013. Pairwise comparisons of ROP treatment in survivors between countries were evaluated by Poisson and multivariable logistic regression analyses after adjustment for confounders. A composite outcome of death or ROP treatment was compared between countries using logistic regression and standardised ratios. RESULTS: Of 48 087 infants included in the analysis, 81.8% survived to 32 weeks postmenstrual age, and 95% of survivors were screened for ROP. Rates of any ROP ranged from 25.2% to 91.0% in Switzerland and Japan, respectively, among those examined. The overall rate of those receiving treatment was 24.9%, which varied from 4.3% to 30.4%. Adjusted risk ratios for ROP treatment were lower for Switzerland in all pairwise comparisons, whereas Japan displayed significantly higher ratios. Comparisons of the composite outcome between countries revealed similar, but less marked differences. CONCLUSIONS: Rates of any ROP and ROP treatment varied significantly between iNeo members, while an overall decline in ROP treatment was observed during the study period. It is unclear whether these variations represent differences in care practices, diagnosis and/or treatment thresholds.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".