Does oral carbohydrate supplementation improve labour outcome? A systematic review and individual patient data meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Labour is a period of significant physical activity. The importance of carbohydrate intake to improve outcome has been recognised in sports medicine and general surgery. OBJECTIVES: To assess the effect of oral carbohydrate supplementation on labour outcomes. SEARCH STRATEGY: MEDLINE (1966-2014), Embase, the Cochrane Library and clinical trial registries. SELECTION CRITERIA: Randomised controlled trials (RCT) of women randomised to receive oral carbohydrate in labour (<6 cm dilated), versus placebo or standard care. DATA COLLECTION AND ANALYSIS: Authors were contacted to provide data. Individual patient data meta-analyses were performed to calculate pooled risk ratios (RR) and 95% confidence intervals (CI). MAIN RESULTS: Eight RCTs met the inclusion criteria. Six authors responded, four supplied data (n = 691). Three studies used isotonic drinks (one placebo-controlled, two compared with standard care), and one an advice booklet regarding carbohydrate intake. The mean difference in energy intake between the intervention and control groups was small [three studies, 195 kilocalories (kcal), 95% CI 118-273]. There was no difference in the risk of caesarean section (RR 1.15, 95% CI 0.83- 1.61), instrumental birth (RR 1.26, 95% CI 0.96-1.66) or syntocinon augmentation (RR 0.99, 95% CI 0.86-1.13). Length of labour was similar (mean difference -3.15 minutes, 95% CI -35.14 to 41.95). Restricting the analysis to primigravid women did not affect the result. Oral carbohydrates did not increase the risk of vomiting (RR 1.09, 95% CI 0.78-1.52) or 1-minute Apgar score <7 (RR 1.23, 95% CI 0.82-1.83). AUTHORS' CONCLUSION: Oral carbohydrate supplements in small quantities did not alter labour outcome. TWEETABLE ABSTRACT: Oral carbohydrate does not affect labour. But the difference between intervention and control equals 10 teaspoons sugar.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".