Increased risk of fungal infection detection in women using menstrual cups vs. tampons: a cross-sectional study
Bibliographic record
Abstract
Abstract Objective To determine if the use of menstrual cups rather than tampons is associated with more or less health risk. Design Analysing biological, demographic, and behavioural data in a cohort of women who reported using mostly tampons ( n = 81) or menstrual cups ( n = 22). Setting A cross-sectional analysis using the inclusion data of a single centre longitudinal study. Population 149 women from 18 to 25 years old living in the area of Montpellier (France) who reported having at least one new sexual partner over the last year. Methods Statistical modelling (mainly binomial regression models and factor analyses of mixed data). Main Outcome Measures Self-reported data from questionnaires (fungal infection, urinary tract infection, stress level) and biological data (HPV screening, vaginal microbiota profiling, circulating antibodies titration, and local cytokine concentrations). Results We identify an increased risk of reporting fungal infections for women using menstrual cups over tampons. We do not detect significant differences in terms of vaginal microbiota composition or local cytokines expression profile but find that women fall into two different clusters in a factor analysis of mixed data depending on the type of menstrual product they use more (cups or tampons). Conclusions These results point to potential health risks in the use of menstrual cups and differences in local vaginal environments. In-depth studies are needed to better understand potential associations between menstrual product use and women’s health. Funding European Research Council (EVOLPROOF, grant 648963) Ethics The PAPCLEAR study ClinicalTrials.gov identifier is NCT02946346 . Tweetable abstract A cross-sectional study finds a significant association between menstrual cup use and fungal infection risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".