Impact of Antibiotic Duration on Gut Microbiome Composition and Antimicrobial Resistance: A Substudy of the BALANCE Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background Maintaining a diverse gut microbiome and minimizing antimicrobial resistance gene (ARG) carriage through reduced antibiotic utilization may decrease antimicrobial resistance. We compared gut microbiome disruption and ARG carriage following 7 or 14 days of antibiotics for treatment of bacteremia in a substudy of the BALANCE randomized controlled trial. Methods The BALANCE randomized controlled trial enrolled 3631 participants with bacteremia, who were randomized 1:1 to receive 7 or 14 days of antibiotics. Rectal swabs were collected from 131 participants and analyzed with metagenomic sequencing to characterize the gut microbiome and ARGs. The primary outcome was change in gut microbiome diversity at day 7 vs 14. Results Forty-one participants (n = 28 in the 14-day group, n = 13 in the 7-day group) had samples available for the primary analysis, with an imbalance in piperacillin-tazobactam exposure between groups. Change in gut microbiome diversity at day 7 vs 14 was comparable between the 14-day group (median, 0.07; IQR, −0.46 to +0.51) and 7-day group (median, 0.19; IQR, −0.77 to +0.22; P = .49). Change in ARG abundance at day 7 vs 14 did not differ by treatment duration, nor did the abundance of individual ARGs. We did not observe any change in gut microbiome diversity or ARG carriage at enrollment vs day 7. Conclusions In this subset of patients from the BALANCE randomized controlled trial, we did not detect greater gut microbiome disruption or ARG carriage among participants who received 14 vs 7 days of antibiotics, but we were limited by small sample size and imbalances between groups.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".