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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it