Sleep duration and quality, and risk of gestational diabetes mellitus in pregnant Chinese women
Bibliographic record
Abstract
AIMS: To examine the association between sleep disturbances during pregnancy and risk of gestational diabetes mellitus. METHODS: From 2010 to 2012, 12 506 women in Tianjin, China, were screened using a 50-g 1-h glucose challenge test at 24-28 weeks' gestation. Those with glucose challenge test values of ≥ 7.8 mmol/l were invited to further undergo a 75-g 2-h oral glucose tolerance test. Gestational diabetes was determined according to the International Association of Diabetes and Pregnancy Study Group's definition. Self-reported sleep duration and sleep quality during pregnancy was documented using a modified questionnaire. Logistic regression was used to obtain odds ratios and 95% CIs. RESULTS: A total of 919 women (7.3%) had gestational diabetes. Sleep duration was found to have an approximate J-shaped association with gestational diabetes risk after adjusting for covariates. Compared with a sleep duration of 7-9 h/day (43% of 12 506 women), the adjusted odds ratios for sleep duration of ≥ 9 h/day (55%) and < 7 h/day (2%) for gestational diabetes were 1.21 (95% CI 1.03-1.42) and 1.36 (95% CI 0.87-2.14), respectively. Compared with good sleep quality (37.9% of 12 506 women), the adjusted odds ratios of moderate (59.9%) and poor sleep quality (2.2%) for gestational diabetes were 1.19 (95% CI 1.01-1.41) and 1.61 (95% CI 1.04-2.50), respectively. CONCLUSION: In pregnant Chinese women, poor sleep quality, and shorter and longer duration of sleep during pregnancy were independently associated with an increased risk of gestational diabetes.
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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 | 0.001 |
| 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".