Bibliographic record
Abstract
OBJECTIVE: Sleeping on sofas increases the risk of sudden infant death syndrome and other sleep-related deaths. We sought to describe factors associated with infant deaths on sofas. METHODS: We analyzed data for infant deaths on sofas from 24 states in 2004 to 2012 in the National Center for the Review and Prevention of Child Deaths Case Reporting System database. Demographic and environmental data for deaths on sofas were compared with data for sleep-related infant deaths in other locations, using bivariate and multivariable, multinomial logistic regression analyses. RESULTS: A total of 1024 deaths on sofas made up 12.9% of sleep-related infant deaths. They were more likely than deaths in other locations to be classified as accidental suffocation or strangulation (adjusted odds ratio [aOR] 1.9; 95% confidence interval [CI], 1.6-2.3) or ill-defined cause of death (aOR 1.2; 95% CI, 1.0-1.5). Infants who died on sofas were less likely to be Hispanic (aOR 0.7; 95% CI, 0.6-0.9) compared with non-Hispanic white infants or to have objects in the environment (aOR 0.6; 95% CI, 0.5-0.7) and more likely to be sharing the surface with another person (aOR 2.4; 95% CI, 1.9-3.0), to be found on the side (aOR 1.9; 95% CI, 1.4-2.4), to be found in a new sleep location (aOR 6.5; 95% CI, 5.2-8.2), and to have had prenatal smoke exposure (aOR 1.4; 95% CI, 1.2-1.6). Data on recent parental alcohol and drug consumption were not available. CONCLUSIONS: The sofa is an extremely hazardous sleep surface for infants. Deaths on sofas are associated with surface sharing, being found on the side, changing sleep location, and experiencing prenatal tobacco exposure, which are all risk factors for sudden infant death syndrome and sleep-related deaths.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".