Neurodevelopmental Outcomes Following Bevacizumab Injections for Retinopathy of Prematurity
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Bevacizumab intravitreal injection, a vascular endothelial growth factor inhibitor, is used to treat retinopathy of prematurity (ROP). However, concerns have been raised regarding its systemic absorption and effect on developing tissues including brain. This study compared neurodevelopment at 18 months' corrected age in preterm infants of <29 weeks' gestation treated with bevacizumab versus laser ablation. METHODS: Data from the Canadian Neonatal Network and the Canadian Neonatal Follow-Up Network databases were retrospectively reviewed. Infants born at <29 weeks' in 2010-2011 with treated ROP were studied. Neurodevelopmental outcome at 18 months was assessed by using neurologic examination and the Bayley Scales of Infant and Toddler Development Third Edition. Regression analyses were performed. RESULTS: Of 125 treated infants, 27 received bevacizumab and 98 laser. The bevacizumab group, compared with laser, obtained a median Bayley Scales of Infant and Toddler Development Third Edition motor composite score of 81 (interquartile range, 70-91) versus 88 (79-97), a language composite score of 79 (65-97) versus 89 (74-97), and a cognitive score of 90 (80-100) versus 90 (85-100). Difference was detected on the motor score only (P = .02). Odds of severe neurodevelopmental disabilities (Bayley scores <70, severe cerebral palsy, hearing aids, or bilateral blindness) was 3.1 times higher (95% confidence interval: 1.2-8.4) in infants treated with bevacizumab versus laser after adjusting for gestational age, gender, maternal education, Score for Neonatal Acute Physiology-II score, bronchopulmonary dysplasia, sepsis, and severe brain injury. CONCLUSIONS: Preterm infants treated with bevacizumab versus laser had higher odds of severe neurodevelopmental disabilities. Further investigation on the long-term safety of antivascular endothelial growth factor treatment of ROP is needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".