Each Additional Day of Antibiotics Is Associated With Lower Gut Anaerobes in Neonatal Intensive Care Unit Patients
Bibliographic record
Abstract
BACKGROUND: Discontinuation of inappropriate antimicrobial therapy is an important target for stewardship intervention. The drug and duration-dependent effects of antibiotics on the developing neonatal gut microbiota needs to be precisely quantified. METHODS: In this retrospective, cross-sectional study, we performed 16S rRNA sequencing on stool swab samples collected from neonatal intensive care unit patients within 7 days of discontinuation of therapy who received ampicillin and tobramycin (AT), ampicillin and cefotaxime (AC), or ampicillin, tobramycin, and metronidazole (ATM). We compared taxonomic composition within term and preterm infant groups between treatment regimens. We calculated adjusted effect estimates for antibiotic type and duration of therapy on the richness of obligate anaerobes and known butyrate-producers in all infants. RESULTS: A total of 72 infants were included in the study. Term infants received AT (20/28; 71%) or AC (8/28; 29%) with median durations of 3 and 3.5 days, respectively. Preterm infants received AT (32/44; 73%) or ATM (12/44; 27%) with median durations of 4 and 7 days, respectively. Compositional analyses of 67 stool swab samples demonstrated low diversity and dominance by potential pathogens. Within 1 week of discontinuation of therapy, each additional day of antibiotics was associated with lower richness of obligate anaerobes (adjusted risk ratio [aRR], 0.84; 95% confidence interval [CI], .73-.95) and butyrate-producers (aRR, 0.82; 95% CI, .67-.97). CONCLUSIONS: Each additional day of antibiotics was associated with lower richness of anaerobes and butyrate-producers within 1 week after therapy. A longitudinally sampled cohort with preexposure sampling is needed to validate our results.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".