Outborns or Inborns: Where Are the Differences? A Comparison Study of Very Preterm Neonatal Intensive Care Unit Infants Cared for in Australia and New Zealand and in Canada
Bibliographic record
Abstract
BACKGROUND: Very preterm infants born outside tertiary centers are at higher risks of adverse outcomes than inborn infants. Regionalization of perinatal care has been introduced worldwide to improve outcomes. OBJECTIVE: To compare the risk-adjusted outcomes of both inborn and outborn infants cared for in tertiary neonatal intensive care units in Australia and New Zealand and in Canada. METHODS: Deidentified data of infants <32 weeks' gestational age from the 29 Australian and New Zealand Neonatal Network units (ANZNN; n = 9,893) and 26 Canadian Neonatal Network units (CNN; n = 7,133) between 2005 and 2007 were analyzed for predischarge adverse outcomes. RESULTS: ANZNN had lower rates of outborns compared to CNN (13 vs. 19%), particularly of late admissions (>2 days of age; 5.8 vs. 22.2% of outborns) who had high morbidity rates. After adjusting for confounding variables including gestation, ANZNN inborn infants had lower odds of chronic lung disease [CLD; 17.0 vs. 23.3%; adjusted odds ratio (AOR) = 0.70, 95% CI: 0.64-0.77], severe neurological injuries on ultrasound (SNI; 4.1 vs. 6.7%; AOR = 0.62, 95% CI: 0.53-0.73), severe retinopathy (5.6 vs. 7%; AOR = 0.71, 95% CI: 0.59-0.84) and necrotizing enterocolitis (3.5 vs. 5.4%; AOR = 0.67, 95% CI: 0.56-0.79), but no difference in mortality odds. After excluding the late outborn admissions, ANZNN outborns had lower odds of SNI (AOR = 0.43, 95% CI: 0.32-0.58) and CLD (AOR = 0.63, 95% CI: 0.49-0.81) than CNN. CONCLUSIONS: ANZNN inborn and early admitted outborn infants had lower odds of neonatal morbidities than their CNN counterparts. However, compared to ANZNN, the higher CNN rates of outborns and their late admissions are likely related to the differences in regionalization and referral practices, and may explain differences in 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".