Maternal and Obstetric Risk Factors for Sudden Infant Death Syndrome in the United States
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to 1). study the incidence of sudden infant death syndrome (SIDS) among singleton births in the United States and 2). identify maternal and obstetric risk factors for SIDS. METHODS: A cohort of all live births in the United States from 1995 to 1998, formed the source population (n = 15627404). The data were obtained from the National Centers for Health Statistics Linked Births and Infant Deaths File. A nested case-control study was used to examine risk factors for SIDS. From this birth cohort, all SIDS deaths (n = 12404) were first identified (case group). From the remaining non-SIDS births, a 4-fold larger sample (n = 49616) was randomly selected as a control group. RESULTS: The overall incidence of SIDS was 81.7 per 100000 live births. More mothers in the case group than in the control group were reported to have placenta previa (odds ratio [OR]: 1.70; 95% confidence interval [CI] 1.24, 2.33), abruptio placentae (OR 1.57; 95% CI 1.24, 1.98), premature rupture of membranes (OR 1.48; 95% CI 1.33, 1.66), or small for gestational age (OR 1.40; 95% CI 1.30, 1.50 for the 10th percentile). SIDS cases were also more likely to be male. Mothers of cases were more likely to be younger, less educated, and nonwhite, and more of them smoked during pregnancy and did not attend prenatal care. CONCLUSION: This analysis confirms the importance of several well known demographic and lifestyle risk factors for SIDS. In addition, placental abnormalities were risk factors for SIDS. LEVEL OF EVIDENCE: II-2
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".