A meta‐analysis of neurodevelopmental outcomes at 4–10 years in children born at 22–25 weeks gestation
Bibliographic record
Abstract
Abstract Aim To update our meta‐analysis on neurodevelopmental disability rates in children born at 22–25 weeks gestation. The main outcome measure was rates of neurodevelopmental disability in survivors at age 4–10 years. Methods We used a peer‐reviewed electronic and grey search to identify articles. Two authors independently reviewed cohorts published after May 2012 with: born ≥1995 in a developed nation; assessed at 4–10 years; prospective; >65% follow‐up; definitions for neurodevelopmental disability as per the EPICure cohort; results reported by gestation. We contacted authors for clarification. Random effects meta‐analysis was used to estimate pooled proportions of neurodevelopmental disability. Within each study, the absolute change in proportions with each week was estimated and then pooled. Results We reviewed 3980 records; 21 articles were assessed and six were included. With the previous 9 cohorts, the meta‐analysis now contains 15. Rates of moderate‐to‐severe neurodevelopmental disability were as follows: 42% (95% CI 23,64%; I2 0%) at 22; 41% (95% CI 31,52%; I2 20%) at 23; 32% (95% CI 25,39%; I2 45%) at 24; 23% (95% CI 18,29%; I2 60%) at 25 weeks. The analysis shows a significant decrease in risk of moderate‐to‐severe neurodevelopmental disability between each week (8.1% (95% CI −11.8, −4.5%); I2 0%; p < 0.001). Conclusion Physicians can use this high‐quality data to support parents during decision‐making.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.063 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".