Effects of prenatal artificial sweeteners consumption on birth outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the influence of prenatal artificial sweetener (AS) consumption on birth outcomes. DESIGN: Systematic review and meta-analysis. SETTING: Online databases (Medline, CINAHL, Embase, Cochrane Library, Scopus, Web of Science, FSTA - the food resource database, and ClinicalTrials.gov) were searched up to 9 April 2020. Studies of all designs (except case studies and reviews) were eligible, which contained information on the relevant population (pregnant women), intervention/exposure (any AS consumption), comparator (no AS consumption) and birth outcomes (preterm delivery, gestational age, birth weight). RESULTS: From 677 citations, ten cohort studies and one randomised controlled trial (n 138 007 women) were included. 'Low' to 'very low' certainty evidence revealed that daily consumption of AS was associated with an increased risk of preterm delivery (three studies, n 129 009; risk ratio = 1·18, 95 % CI 1·09, 1·28, I2 = 9 %), a 24 g increase in birth weight (three studies, n 64 417; mean difference (MD): 23·74 g, 95 % CI 0·89, 45·58, I2 = 0 %) and a 0·11 week decrease in gestational age (three studies, n 64 417; MD: -0·11 weeks, 95 % CI -0·19, -0·03, I2 = 0 %). CONCLUSIONS: 'Low' to 'very low' certainty evidence suggests daily AS consumption during pregnancy is associated with an increased risk of preterm delivery, increased birth weight and decreased gestational age. Additional 'high'-quality research is urgently needed to further assess these relationships.PROSPERO registration number: CRD42019136728.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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 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".