Demographic Risk Factors of Retinopathy of Prematurity: A Systematic Review of Population-Based Studies
Bibliographic record
Abstract
INTRODUCTION: Current national guidelines use gestational age (GA) and birth weight (BW) as their basis for retinopathy of prematurity (ROP) screening. The strength of association of these and other demographic risk factors is inconsistent across studies. This review aims to evaluate the strength of association of documented risk factors for ROP in large sample, population-based studies. METHODS: MEDLINE, EMBASE, and Cochrane Library were searched from January 2010 to May 2020. Original studies reporting the risk of ROP in a region and demographic risk factors were included. RESULTS: Eighteen studies comprising 342,005 infants were included. The overall risk of ROP in preterm infants was 18.8%. For every week decrease in GA, there was a median adjusted odds ratio (aOR) of 1.4 times (range 1.2-1.9) of developing ROP. For every 100-g decrease in BW, the median aOR was 1.8 times (range 1.2-2.7). Higher risk was found in infants with neonatal sepsis and bronchopulmonary dysplasia. The risk of any, severe, and treatment-requiring ROP was highest for 23 weeks GA, which was 66.5, 40.3, and 39.4%, respectively. Regions with higher neonatal mortality rates had the highest mean GA of infants with ROP. CONCLUSION: For every week decrease in GA and every 100-g decrease in BW, there was a median of 1.4 times and 1.8 times the odds of developing ROP, respectively. Further research is required to clarify the role of additional risk factors.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".