NIDCAP: A Systematic Review and Meta-analyses of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The "synactive" theory of neurobehavioral development forms the basis of the Newborn Individualized Developmental Care and Assessment Program (NIDCAP). Our objective was to assess the effectiveness of NIDCAP in improving outcomes in preterm infants. METHODS: Medline, CINAHL, Embase, PsychInfo, The Cochrane Library, Pediatric Academic Societies' Abstracts and Web of Science were searched in July 2010 and February 2012. The studies selected were randomized controlled trials testing the effectiveness of NIDCAP on medical and neurodevelopmental outcomes. The authors abstracted baseline characteristics of infants and outcomes. The risk of bias was assessed by using Cochrane criteria. RevMan 5.1 was used to synthesize data by the use of relative risk and risk difference for dichotomous outcomes and mean or standardized mean difference for continuous outcomes. RESULTS: Eleven primary and 7 secondary studies enrolling 627 neonates were included, with 2 of high quality. The composite primary outcomes of death or major sensorineural disability at 18 months corrected age or later in childhood (3 trials, 302 children; relative risk 0.89 [95% confidence interval 0.61 to 1.29]) and survival free of disability at 18 months corrected age or later in childhood (2 trials, 192 infants; relative risk 0.97 [95% confidence interval 0.69 to 1.35]), were not significantly different between the NIDCAP and control groups. With the sensitivity analysis that excluded the 2 statistically heterogeneous outlying studies, there were no significant differences between groups for short-term medical outcomes. CONCLUSIONS: This systematic review including 627 preterm infants did not find any evidence that NIDCAP improves long-term neurodevelopmental or short-term medical outcomes.
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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 | 0.042 | 0.107 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.030 | 0.035 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".