Associations between Polycystic Ovary Syndrome (PCOS) and Antibiotic Use: Results from the UAEHFS
Bibliographic record
Abstract
Women with polycystic ovary syndrome (PCOS) have a higher susceptibility to infections compared to those without PCOS. Studies evaluating antibiotic use based on PCOS status are scarce. Therefore, we aimed to (i) assess the associations between self-reported PCOS and antibiotic use, and (ii) whether PCOS treatment and the age at PCOS diagnosis modified the associations above. This cross-sectional analysis used the United Arab Emirates Healthy Future Study (UAEHFS) conducted from February 2016 to March 2023 involving 2063 Emirati women aged 18-62 years. We performed ordinal logistic regressions under unadjusted and demographic-health-characteristic-adjusted models to obtain the odds ratios (ORs) and 95% confidence intervals (CIs) to analyze PCOS and antibiotic use. Subgroup analyses were performed by treatment status and age at diagnosis. We found that women with PCOS were 55% more likely to frequently take a course of antibiotics in the past year (aOR 1.55; 95% CI 1.26-1.90). Similar likelihoods were also found among those being treated for PCOS and those without treatment but with a PCOS diagnosis at ≤25 years. Our study suggests that PCOS was associated with an increased use of antibiotics among Emirati women. Understanding the frequent antibiotic use susceptibility among those with PCOS may improve antibiotic use surveillance and promote antibiotic stewardship in these at-risk individuals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".