Antibiotic exposure and risk of weight gain and obesity: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: The prevalence of obesity is increasing worldwide, and there is growing interest in better delineating the role of the human gut microbiome in this phenomenon. Obesity-specific gut microbiome features have been observed in both human and animal studies, and these variations appear to play a causative role in increasing body weight. There is evidence that antibiotics can modify the composition and diversity of the gut microbiome and that this may contribute to body weight changes. The primary objective of the proposed systematic review is to evaluate and synthesize the existing evidence evaluating the possible association between antibiotic use, weight gain, and obesity. METHODS: A comprehensive search of the MEDLINE and EMBASE databases will be performed. Both randomized and non-randomized studies (excluding case reports) in neonates, children, adults, and pregnant women will be included. The exposure of interest is antibiotics of any type, duration, and route given for any indication. All included studies must have a comparator group. The primary outcomes are the development of overweight and obesity. Secondary outcomes are percent weight-change from baseline and change in body mass index or waist circumference. Additional secondary outcomes in pregnant women are gestational weight gain, postpartum weight retention, offspring birth weight, childhood weight, and obesity. Risk of bias of included trials will be performed. Two reviewers will screen and perform data extraction independently. DISCUSSION: This systematic review will summarize the existing evidence evaluating the association between antibiotic use, weight gain, and obesity and facilitate the identification of important gaps and uncertainties in the literature. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017069177.
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 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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".