Knowledge of Vulvar Anatomy and Self-examination in a Sample of Italian Women
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to investigate the knowledge of vulvar anatomy and vulvar self-examination (VSE) in a sample of Italian women attending a gynecology clinic. METHODS: For this original research from May to July 2019, 512 women attending the Lower Genital Tract Clinic at the Department of Surgical Sciences of the University of Torino were invited to participate in a 29-question survey about vulvar anatomy, VSE, and sociodemographic details. Data were analyzed using descriptive statistics. RESULTS: Of 512 patients, 500 completed the questionnaire (98% response rate). The mean age of respondents was 41 years (range = 17-77 years). Education level was evenly distributed between elementary, high school, and university graduates. Only 15% of interviewed women were able correctly sketching vulvar anatomy. Seventy-six percent of the women had not heard about VSE, and 61% of the women approach their genitalia with feelings of shame and embarrassment. Only 23% of the women would seek medical advice after identification of possible abnormalities during VSE. A majority (69%) of the women would like to have more information about VSE and vulvar health through educational videos and social media. CONCLUSIONS: Education about VSE may lead to earlier diagnosis of vulvar cancers and other pathologies. Further efforts are needed to disperse information about normal external female genital anatomy and VSE to achieve self-confidence among women.
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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 | 0.000 |
| 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 | 0.000 | 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".