81: Neurodevelopmental Outcomes of Extremely Preterm Infants Treated with Bevacizumab for Severe Retinopathy of Prematurity
Bibliographic record
Abstract
Intravitreal injection of bevacizumab, a vascular endothelial growth factor inhibitor (VEGF), is used to treat retinopathy of prematurity (ROP). As bevacizumab can diffuse into the systemic circulation, potential long-term effect on brain development needs to be documented. To compare neurodevelopmental outcomes at 18–22 months of preterm infants treated with bevacizumab versus laser. Data from the Canadian Neonatal Network and the Canadian Neonatal Follow-Up Network databases were retrospectively reviewed. A total of 114 infants born at <29 weeks gestational age (GA) in 2010–2011 with severe ROP (≥ stage 3 or plus disease) requiring treatment and followed at 18–22 months corrected age (CA) were studied. Neurodevelopmental outcome was assessed using the Bayley Scales 3rd edition. Regression analyses were performed. Of the 114 infants, 32 had bevacizumab (GA 24.8±1.5 weeks, birth weight 740±160 g) and 82 had laser (GA 24.8±1.3 weeks, birth weight 711±132 g). Neonatal characteristics differed between the bevacizumab vs. laser therapy groups for male sex (62% vs. 42%), SNAP-II score (24 vs. 18), and late-onset sepsis (62% vs. 44%). Bevacizumab treated infants had lower motor scores after adjustment for potential confounders (table). Preterm infants treated with bevacizumab had lower motor scores compared to those treated with laser therapy. There was no difference in cognition and language scores. Further investigation on the long-term safety of anti-VEGF treatment for ROP is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".