Change in neurodevelopmental outcomes for extremely premature infants over time: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Survival rates of extremely premature infants are rising, but changes in neurodevelopmental impairment (NDI) rates are unclear. Our objective was to perform a systematic review of intrainstitutional variability of NDI over time. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Ovid MEDLINE, Embase, PubMed, Cochrane Library and Google Scholar. STUDY SELECTION: Study eligibility: (1) at least two discrete cohorts of infants born <27 weeks' gestation or <1000 g birth weight, (2) one cohort born after 1990 and at least one subsequent cohort of similar gestational age, (3) all cohorts cared for within the same Neonatal Intensive Care Unit(s) (NICU) and (4) neurodevelopmental outcomes at 18-36 months corrected age. MAIN OUTCOME: Change in NDI rates. Quality, validity and bias were assessed using Grading of Recommendations, Assessment, Development, and Evaluation and Quality in Prognosis Studies guidelines. RESULTS: Of 203 publications, 15 were eligible, including 13 229 infants. At the first time point, average NDI rate across study groups weighted by sample size was 41.0% (95% CI 34.0% to 48.0%). The average change in NDI between time points was -3.3% (95% CI -8·8% to 2.2%). For each added week of gestation at birth, the rate of NDI declined by 9.7% (95% CI 6.2% to 13.3%). Most studies exhibited moderate-severe bias in at least one domain, especially attrition rates. CONCLUSIONS: When comparing discrete same-centre cohorts over time, there was no significant change in NDI rates in infants born <27 weeks' gestation or <1000 g. Higher survival rates unaccompanied by improvement in neurodevelopment highlight urgency for renewed focus on the causes of NDI and evidence-based strategies to reduce brain injury.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".