Maternal sleep duration and neonate birth weight: A population‐based cohort study
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between maternal sleep duration (an important health indicator) and neonate birth weight. METHODS: The study included 2536 mother-neonate pairs of a Spanish birth cohort (2004-2006, INMA project). The exposures were questionnaire-based measures of sleep duration before and during pregnancy. The primary outcome was neonate birth weight score (g) standardized to 40 weeks of gestation. RESULTS: In women sleeping for <7 h/day before pregnancy, each additional hour of sleep increased birth weight score by 44.7 g (P = 0.049) in the minimally adjusted model, although findings were not statistically significant after considering other potential confounders (P > 0.05). However, increasing sleep duration for the group of mothers who slept for more than 9 h/day decreased birth weight score by 39.2 g per additional hour (P = 0.001). Findings were similar after adjusting for several sociodemographic confounders and maternal depression-anxiety clinical history as an intermediate factor. Similar but attenuated associations were observed with sleep duration in the second trimester of pregnancy. CONCLUSION: The relationship between maternal sleep duration before and during pregnancy and neonate birth weight is an inverse U-shaped curve. Excessive sleep duration may adversely affect neonate health through its impact on birth weight.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".