Effect of intrapartum azithromycin on early childhood gut mycobiota development: post hoc analysis of a double-blind randomized trial
Bibliographic record
Abstract
Intrapartum azithromycin prophylaxis reduced maternal infections but showed no effect on neonatal sepsis and mortality. Although antibiotic exposure may indirectly alter the mycobiota (community of fungi that live in a given environment), there is no data available on how intrapartum azithromycin impacts gut mycobiota development. We hereby assess the impact of intrapartum azithromycin on gut mycobiota development from birth to the age of three years, by ITS2 gene profiling of rectal samples from 102 healthy Gambian infants selected from a double-blind randomized placebo-controlled clinical trial (PregnAnZI-2 - ClinicalTrials.org NCT03199547). In the trial, women received 2 g oral azithromycin or placebo (1:1) during labour with the intension of assessing effect on neonatal sepsis or mortality. Secondary objectives included effects on bacterial carriage and resistance, puerperal infections, and infant growth. Our analysis show that season and parity were key factors that influenced gut mycobiota development. Intrapartum azithromycin increased the abundance of Candida orthopsilosis but only in the wet season and did not show different effects by sex of the child. These data suggest that season and parity can be key factors influencing gut mycobiota development and may inform strategies for a wider implementation of intrapartum azithromycin intervention.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".