Trends in Cause-Specific Mortality at a Canadian Outborn NICU
Bibliographic record
Abstract
OBJECTIVE: To retrospectively review changes in the causes of death of infants dying in the NICU at Canada's largest outborn pediatric center. PATIENTS AND METHODS: All inpatient deaths at the Hospital for Sick Children's NICU that occurred in the years 1997, 2002, and 2007 were retrospectively reviewed to identify the primary cause of death. Classification of the cause of death was based on a modified version of the Perinatal Society of Australia and New Zealand's Neonatal Death Classification. RESULTS: The annual mortality rate remained relatively constant (average of 7.6 deaths per 100 admissions between 1988 and 2007). A total of 156 deaths were analyzed: 53 in 1997; 50 in 2002; and 53 in 2007. The chronological age at which premature infants died increased significantly over the 3 time periods (P = .01). The proportion of deaths attributable to extreme prematurity and intraventricular hemorrhage decreased over the study period, whereas the proportion of deaths attributed to gastrointestinal causes (specifically necrotizing enterocolitis and focal intestinal perforation) increased. The proportion of infants for whom there was a decision to limit care before death was stable at between 83% and 92%. CONCLUSIONS: A larger proportion of outborn premature infants admitted to the Hospital for Sick Children's NICU seem to be surviving the early problems of prematurity only to succumb to late complications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".