Variations in mortality rates among Canadian neonatal intensive care units.
Bibliographic record
Abstract
BACKGROUND: Most previous reports of variations in mortality rates for infants admitted to neonatal intensive care units (NICUs) have involved small groups of subpopulations, such as infants with very low birth weight. Our aim was to examine the incidence and causes of death and the risk-adjusted variation in mortality rates for a large group of infants of all birth weights admitted to Canadian NICUs. METHODS: We examined the deaths that occurred among all 19 265 infants admitted to 17 tertiary-level Canadian NICUs from January 1996 to October 1997. We used multivariate analysis to examine the risk factors associated with death and the variations in mortality rates, adjusting for risks in the baseline population, severity of illness on admission and whether the infant was outborn (born at a different hospital from the one where the NICU was located). RESULTS: The overall mortality rate was 4% (795 infants died). Forty percent of the deaths (n = 318) occurred within 2 days of NICU admission, 50% (n = 397) within 3 days and 75% (n = 596) within 12 days. The major conditions associated with death were gestational age less than 24 weeks (59 deaths [7%]), gestational age 24-28 weeks (325 deaths [41%]), outborn status (340 deaths [42%]), congenital anomalies (270 deaths [34%]), surgery (141 deaths [18%]), infection (108 deaths 114%]), hypoxic-ischemic encephalopathy (128 deaths [16%]) and small for gestational age (i.e., less than the third percentile) (77 deaths [10%]). There was significant variation in the risk-adjusted mortality rates (range 1.6% to 5.5%) among the 17 NICUs. INTERPRETATION: Most NICU deaths occurred within the first few days after admission. Preterm birth, outborn status and congenital anomalies were the conditions most frequently associated with death in the NICU. The significant variation in risk-adjusted mortality rates emphasizes the importance of risk adjustment for valid comparison of NICU outcomes.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".