A Meta-Analysis of Neurodevelopmental Outcomes following Intravitreal Bevacizumab for the Treatment of Retinopathy of Prematurity
Bibliographic record
Abstract
BACKGROUND: Retinopathy of prematurity (ROP) is the most common cause of preventable blindness in preterm infants. First-line treatments include intravitreal bevacizumab (IVB) or laser photocoagulation (LPC). OBJECTIVES: The aim of the study was to evaluate neurodevelopmental safety of IVB compared to LPC for ROP. METHODS: MEDLINE, Embase, and Cochrane library were searched up to September 2022. Studies were included with at least 12-month follow-up of primary outcomes such as severe neurodevelopmental impairment (sNDI), cerebral palsy (CP), and hearing impairment (HI). Secondary outcomes were moderate-to-severe neurodevelopmental impairment (msNDI), Bayley Scores of Infant Development (BSID-III), and visual impairment. RESULTS: 1,231 patients from 11 comparative studies were included. Quality of evidence was rated low for all outcomes. IVB was associated with a higher risk for sNDI (risk ratio [RR] = 1.25, 95% confidence interval [CI]: [1.01, 1.53], p = 0.04); and CP (RR = 1.40, CI: [1.08, 1.81], p = 0.01) compared to LPC. There was no significant difference between IVB and LPC for msNDI (RR = 1.15, CI: [0.98, 1.35], p = 0.08) and HI (RR = 1.43, CI: [0.86, 2.39], p = 0.17). BSID-III percentile scores were similar between IVB and LPC, with weighted mean differences of 1.51 [CI = -1.25, 4.27], 2.43 [CI = -1.36, 6.22], and 1.97 [CI = -1.06, 5.01] for cognitive, language, and motor domains, respectively (p > 0.05). CONCLUSION: To our knowledge, this is the largest meta-analysis on neurodevelopmental outcomes and the first to rigorously examine IVB monotherapy in ROP treatment. Compared to LPC, there was a marginally increased risk for sNDI and CP with IVB but little or no difference in the risk of msNDI and HI. Further randomized studies are needed to strengthen these findings.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.052 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".