The mortality of very low birth weight infants: the benefit and relative impact of changes in population and therapeutic variables
Bibliographic record
Abstract
OBJECTIVE: Very low birth weight (VLBW, ≤1500 g) infants' mortality rates have decreased markedly. We aimed to quantify the relative contribution of changes in the distribution of population characteristics and changes in specific mortality rates on the decline in mortality rates of VLBW infants. STUDY DESIGN: A population-based observational study of the Israel national VLBW infant database. The study population comprised singleton VLBW infants of 24-32 weeks' gestation born during the epochs 1995-2000 (n = 3728) and 2006-2010 (n = 3246). The Kitagawa methodology was applied to determine the contribution of changes in demographic and perinatal characteristics and changes in specific mortality rates on the decline in mortality between the periods. RESULTS: During the study epochs, VLBW infant mortality rates decreased from 19.7 to 13.8%. Of the 5.9% decrease in mortality, 60.6% was attributed to the decrease in specific mortality rates and 39.4% to changes in the proportions of population characteristics and therapies, predominantly early initiation of prenatal care (8.1%), antenatal steroids (25.1%), and cesarean delivery (8.1%). For most of the demographic and perinatal categories considered the relative contribution of changes in their proportions was <3%, whereas >97% could be attributed to changes in the specific mortality rates for these characteristics. CONCLUSIONS: The decrease in preterm VLBW infant mortality was attributable predominantly to changes in variable specific mortality rates whereas changes in the proportions of demographic, perinatal risk factors, and therapies had a limited impact on VLBW infant mortality. Future assessment of determinants of VLBW infant mortality data should be dissected by discriminatory models.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".