Actuarial survival of a large Canadian cohort of preterm infants
Bibliographic record
Abstract
BACKGROUND: The increased survival of preterm and very low birth weight infants in recent years has been well documented but continued surveillance is required in order to monitor the effects of new therapeutic interventions. Gestation and birth weight specific survival rates most accurately reflect the outcome of perinatal care. Our aims were to determine survival to discharge for a large Canadian cohort of preterm infants admitted to the neonatal intensive care unit (NICU), and to examine the effect of gender on survival and the effect of increasing postnatal age on predicted survival. METHODS: Outcomes for all 19,507 infants admitted to 17 NICUs throughout Canada between January 1996 and October 1997 were collected prospectively. Babies with congenital anomalies were excluded from the study population. Gestation and birth weight specific survival for all infants with birth weight < 1,500 g (n = 3419) or gestation < or = 30 weeks (n = 3119) were recorded. Actuarial survival curves were constructed to show changes in expected survival with increasing postnatal age. RESULTS: Survival to discharge at 24 weeks gestation was 54%, compared to 82% at 26 weeks and 95% at 30 weeks. In infants with birth weights 600-699, survival to discharge was 62%, compared to 79% at 700-799 g and 96% at 1,000-1,099 g. In infants born at 24 weeks gestational age, survival was higher in females but there were no significant gender differences above 24 weeks gestation. Actuarial analysis showed that risk of death was highest in the first 5 days. For infants born at 24 weeks gestation, estimated survival probability to 48 hours, 7 days and 4 weeks were 88 (CI 84, 92)%, 70 (CI 64, 76)% and 60 (CI 53, 66)% respectively. For smaller birth weights, female survival probabilities were higher than males for the first 40 days of life. CONCLUSION: Actuarial analysis provides useful information when counseling parents and highlights the importance of frequently revising the prediction for long term survival particularly after the first few days of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".