Pre-eclampsia and the risk of retinopathy of prematurity in preterm infants with birth weight <1500 g and/or <31 weeks’ gestation
Bibliographic record
Abstract
Objective To evaluate the relationship between pre-eclampsia and development of retinopathy of prematurity (ROP) in infants with birth weight of <1500 g and/or gestation <31 weeks. Methods A retrospective cohort study comprising infants born to mothers with pre-eclampsia between January 2007 and June 2010 at a single tertiary care centre. Their ROP outcome was compared with infants born to the next two normotensive mothers with a ±1 week gestational age difference. Pearson χ2 test was used for categorical variables and Mann-Whitney U test was used for continuous variables. Multivariable regression was used to estimate the OR of ROP with prenatal pre-eclampsia exposure and adjust for confounders. Results Of the 97 infants in the pre-eclampsia group, 27 (27%) developed ROP and of the 185 infants in the normotensive group, 50 (27%) developed ROP. On multivariable regression modelling, pre-eclampsia was not a risk factor for the development of ROP (OR 1.4, 95% CI 0.46 to 4.1). Gestational age, intrauterine growth restriction and blood transfusion were significant risk factors for the development of ROP. Conclusions In our cohort, pre-eclampsia was not a significant risk factor for the development of ROP. Intrauterine growth restricted infants of pre-eclamptic and normotensive mothers were at higher risk of 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".