Maternal sleep-disordered breathing and the risk of delivering small for gestational age infants: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: It is unclear whether objectively measured maternal sleep-disordered breathing (SDB) leads to poor fetal outcomes. In this study, we prospectively assessed whether polysomnography-based diagnosis of SDB in the third trimester is associated with the delivery of small for gestational age (SGA) infants. STUDY DESIGN: Participants were recruited from a multicentre pregnancy cohort study. Eligible participants were evaluated for SDB based on symptoms (snoring and/or witnessed apnoeas assessed using the Pittsburgh Sleep Quality Index questionnaire) and in-home complete polysomnography in the third trimester. SGA was defined as <10th centile using customised birthweight centiles adjusted for maternal parity, prepregnancy body mass index (BMI), ethnicity, gestational age and infant sex. RESULTS: Of the 234 pregnant participants who completed a sleep study, 82% were Caucasian, with mean (SD) age of 31 (4.3) years and a prepregnancy BMI of 23 (4) kg/m(2). The delivery of SGA infants occurred in 27 (12%) of the study participants. The symptoms of SDB had poor overall sensitivity and specificity for diagnosing SDB identified by polysomnography. Symptoms of SDB in the third trimester demonstrated a potential association with delivering an SGA infant, however this did not reach statistical significance (OR 2.36 (95% CI 0.85 to 6.54, p=0.10)). However, the odds of delivering an SGA infant were significantly increased with polysomnography-based diagnosis of maternal SDB (using apnoea-hypopnoea index cut-off of 10, OR 2.65 (95% CI 1.15 to 6.10, p=0.02)). CONCLUSIONS: Objectively measured SDB in the third trimester is significantly associated with the delivery of SGA infants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".