“Check Your Vulva”—A Patient Education and Virtual Vulva Care Pilot Project
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to identify whether vulvar self-examination learned from a web site could lead to a self-identification of vulvar lesions and the feasibility of virtual vulvar care with patient submitted photos. MATERIALS AND METHODS: The study used a prospective cohort design in a tertiary academic hospital over a 1-year period. Eligible participants who self-identified a vulvar lesion/skin changes were invited to send vulvar photos through a secure patient portal and schedule a phone consult to discuss diagnosis/management. Clinical data, photo interpretability, and patient satisfaction measures were collected. Self-referral patients versus vulva clinic waitlist patients were analyzed separately. RESULTS: Few people were interested in submitting vulvar photos online. Twenty-eight participants directly contacted the study, 8 consented, and 6 sent in vulvar photos. Forty four of 476 on the waitlist consented but only 24 of 44 sent in photos (5% of waitlist patients). The median time for a virtual assessment was 7 days for study participants while it was 18 months for the in-person usual care pathway. Most patient submitted photos were assessable. However, 60% participants needed help from another person to take the photos. More than 90% of patients required an in-person visit for their vulvar condition/concerns. While most patients were happy with the virtual process, 58% rated their satisfaction with the ease of taking photos of the genital region as "fair" or "poor." CONCLUSIONS: Virtual care with photos/phone calls might be feasible, although most patients are unlikely to participate. Because of patient discomfort, unease with taking photos, and patient privacy concerns, vulvar care should continue to be in-person for most new consults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".