Sudden infant death syndrome: a re-examination of temporal trends
Bibliographic record
Abstract
BACKGROUND: While the reduction in infants' prone sleeping has led to a temporal decline in Sudden Infant Death Syndrome (SIDS), some aspects of this trend remain unexplained. We assessed whether changes in the gestational age distribution of births also contributed to the temporal reduction in SIDS. METHODS: SIDS patterns among singleton and twin births in the United States were analysed in 1995-96 and 2004-05. The temporal reduction in SIDS was partitioned using the Kitagawa decomposition method into reductions due to changes in the gestational age distribution and reductions due to changes in gestational age-specific SIDS rates. Both the traditional and the fetuses-at-risk models were used. RESULTS: SIDS rates declined with increasing gestation under the traditional perinatal model. Rates were higher at early gestation among singletons compared with twins, while the reverse was true at later gestation. Under the fetuses-at-risk model, SIDS rates increased with increasing gestation and twins had higher rates of SIDS than singletons at all gestational ages. Between 1995-96 and 2004-05, SIDS declined from 8.3 to 5.6 per 10,000 live births among singletons and from 14.2 to 10.6 per 10,000 live births among twins. Decomposition using the traditional model showed that the SIDS reduction among singletons and twins was entirely due to changes in the gestational age-specific SIDS rate. The fetuses-at-risk model attributed 45% of the SIDS reduction to changes in the gestational age distribution and 55% of the reduction to changes in gestational age-specific SIDS rates among singletons; among twins these proportions were 64% and 36%, respectively. CONCLUSION: Changes in the gestational age distribution may have contributed to the recent temporal reduction in SIDS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".