Process and Outcome Measures for Infants Born Moderate and Late Preterm in Tertiary Canadian Neonatal Intensive Care Units
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence of and between-center variations in care practices and clinical outcomes of moderate and late preterm infants (MLPIs) admitted to tertiary Canadian neonatal intensive care units (NICUs). STUDY DESIGN: weeks of gestation and admitted to 25 NICUs participating in the Canadian Neonatal Network between 2015 and 2020. Patient characteristics, process measures represented by care practices, and outcome measures represented by clinical in-hospital and discharge outcomes were reported by gestational age weeks. NICUs were compared using indirect standardization after adjustment for patient characteristics. RESULTS: Among 25 669 infants (17% of MLPIs born in Canada during the study period) included, 45% received deferred cord clamping, 7% had admission hypothermia, 47% received noninvasive respiratory support, 11% received mechanical ventilation, 8% received surfactant, 40% received antibiotics in the first 3 days, 4% did not receive feeding in the first 2 days, and 77% had vascular access. Mortality, early-onset sepsis, late-onset sepsis, or necrotizing enterocolitis occurred in <1% of the study cohort. Median (IQR) length of stay was 14 (9-21) days among infants discharged home from the admission hospital and 5 (3-9) days among infants transferred to community hospitals. Among infants discharged home, 33% were discharged on exclusive breastmilk and 75% on any breastmilk. There were significant variations between NICUs in all process and outcome measures. CONCLUSIONS: Care practices and outcomes of MLPIs varied significantly between Canadian NICUs. Standardization of process and outcome quality measures for this population will enable benchmarking and research, facilitating systemwide improvements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".