Perinatal Infections and Neurodevelopmental Outcome in Very Preterm and Very Low-Birth-Weight Infants
Bibliographic record
Abstract
IMPORTANCE: Perinatal infections are commonly present in preterm and very low-birth-weight (VLWB) infants and might contribute to adverse neurodevelopmental outcome. OBJECTIVE: To summarize studies evaluating the effect of perinatal infections on neurodevelopmental outcome in very preterm/VLBW infants. EVIDENCE REVIEW: On December 12, 2011, we searched Medline, PsycINFO, Embase, and Web of Knowledge for studies on infections and neurodevelopmental outcome. All titles and abstracts were assessed for eligibility by 2 independent reviewers. We also screened the reference lists of identified articles to search for additional eligible studies. Preselected criteria justified inclusion in this meta-analysis: (1) the study included infants born very preterm (≤32 weeks) and/or with VLBW (≤1500 g); (2) the study compared infants with and without perinatal infection; (3) there was follow-up using the Bayley Scales of Infant Development 2nd edition; and (4) results were published in an English-language peer-reviewed journal. The quality of each included study was assessed using the Newcastle-Ottawa Scale. FINDINGS: This meta-analysis includes 18 studies encompassing data on 13.755 very preterm/VLBW infants. Very preterm/VLBW infants with perinatal infections had poorer mental (d = -0.25; P < .001) and motor (d = -0.37; P < .001) development compared with very preterm/VLBW infants without infections. Mental development was most impaired by necrotizing enterocolitis (d = -0.40; P < .001) and meningitis (d = -0.37; P < .001). Motor development was most impaired by necrotizing enterocolitis (d = -0.66; P < .001). Chorioamnionitis did not affect mental (d = -0.05; P = .37) or motor (d = 0.19; P = .08) development. CONCLUSIONS AND RELEVANCE: Postnatal infections have detrimental effects on mental and motor development in very preterm/VLBW infants.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".