Improvement of Short- and Long-Term Outcomes for Very Low Birth Weight Infants: Edmonton NIDCAP Trial
Bibliographic record
Abstract
OBJECTIVE: Our objective was to determine the impact of Newborn Individualized Developmental Care and Assessment Program (NIDCAP)-based care on length of stay of very low birth weight (VLBW) infants. Secondary outcome measures were days of ventilation, incidence of chronic lung disease, and 18-month neurodevelopmental outcomes. METHODS: This cluster-randomized, controlled trial took place in a large NICU in Canada, with follow-up evaluation at 18 months of age, from September 1999 to September 2004. One hundred VLBW singleton infants and 10 VLBW twin sets were assigned randomly to NIDCAP-based or control care, and 90% participated in follow-up assessments. The intervention was NIDCAP-based care (N = 56), that is, care by NIDCAP-educated staff members and behavioral observations. The control group (N = 55) received standard NICU care. Statistical analyses were adjusted for cluster randomization. Although the intervention was not blinded, the pediatricians making the decisions to discharge the infants were not involved in the study, and the follow-up staff members were blinded with respect to group. RESULTS: NIDCAP group infants had reduced length of stay (median: NIDCAP: 74 days; control: 84 days; P = .003) and incidence of chronic lung disease (NIDCAP: 29%; control: 49%; odds ratio: 0.42 [95% confidence interval: 0.18-0.95]; P = .035). At 18 months of adjusted age, NIDCAP group infants had less disability, specifically mental delay (NIDCAP: 10%; control: 30%; odds ratio: 0.25 [95% confidence interval: 0.08-0.82]; P = .017). CONCLUSION: NIDCAP-based care for VLBW infants improved short- and long-term outcomes significantly.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".