What risk factors for sudden infant death syndrome are preterm and term medically complex infants exposed to at home?
Bibliographic record
Abstract
Abstract Objectives Risk factors for sudden infant death syndrome include premature birth, maternal smoking, prone or side sleeping position, sleeping with blankets, sharing a sleeping surface with an adult, and sleeping without an adult in the room. In this study, we compare parents’ responses on sleep patterns in premature and term infants with medical complexity. Methods Parents of children enrolled in the Canadian Respiratory Syncytial Virus Evaluation Study of Palivizumab were phoned monthly regarding their child’s health status until the end of each respiratory syncytial virus season. Baseline data were obtained on patient demographics, medical history, and neonatal course. Responses on adherence to safe sleep recommendations were recorded as part of the assessment. Results A total of 2,526 preterms and 670 term infants with medical complexity were enrolled. Statistically significant differences were found in maternal smoking rates between the two groups: 13.3% (preterm); 9.3% (term) infants (χ 2=8.1, df=1, P=0.004) and with respect to toys in the crib: 12.3% (term) versus 5.8% preterms (χ 2=24.5, df=1, P<0.0005). Preterm infants were also significantly more likely to be placed prone to sleep (8.8%), compared with term infants (3.3%), (χ 2=18.1, df=1, P<0.0005). Conclusion All the infants in this study had frequent medical contacts. There is a greater prevalence of some risk factors for sudden infant death syndrome in preterm infants compared to term infants with medical complexity. Specific educational interventions for vulnerable infants may be necessary.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".