Ease, Comfort, and Performance of the HerSwab Vaginal Self-Sampling Device for the Detection of Chlamydia trachomatis and Neisseria gonorrhoeae
Bibliographic record
Abstract
BACKGROUND: Many sexually transmitted diseases are asymptomatic in the lower genital tract and can cause upper tract complications if left untreated. Self-collected vaginal (SCV) swabs enable the accurate detection of many sexually transmitted infections and give women the option of collecting their own samples while providing them with privacy and convenience. METHODS: We compared SCV samples collected and transported dry using the HerSwab device to physician-collected vaginal (PCV) Aptima swabs for the detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG), and measured patients' ease and comfort with self-collection. A total of 189 women aged 16 to 41 years were consented into the study and answered a standardized anonymized questionnaire regarding self-collection with the HerSwab device. RESULTS: Women reported self-collection with HerSwab to be easy (97.1%) and comfortable (88.3%). They preferred self-collection over physician collection (80.9%) and would consider using HerSwab for self-collection at home (79.7%). Samples of SCV and PCV showed an overall agreement of 94.7% (κ = 0.64) for CT and of 98.4% (κ = 0.56) for NG, and HerSwab collection detected 7 more positive patients than PCV collection. The overall prevalence of infection was 10.6% for CT and 2.6% for NG. CONCLUSION: HerSwab SCV samples are suitable for the diagnosis of CT and NG.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".