Current Practices on Diagnosis and Management of Women With Vulvodynia
Bibliographic record
Abstract
Abstract Introduction and hypothesis : To describe clinical characteristics, previous medical assessment, past treatments and vulvar pain relief among women with vulvodynia. Methods Brazilian women with vulvodynia (n = 144) were assessed for vaginal infection and vulvar pain intensity by means of a cotton swab test based on a numerical rate scale (NRS). All women answered the Female Sexual Function Index questionnaire and a structured instrument about present vulvar symptoms and previously experienced treatments. Vulvar pain relief achieved with previous treatments was qualified through a 4-point Likert-scale. Results Previous vulvar pain duration was 5.8 (± 4) years. More than 50% consulted with three or more physicians and 49% remained without a conclusive diagnosis. Diagnosis and treatment vulvovaginal infection was very common. The most commonly used treatments were lubricants (66%), topical anesthetics (36%) and vulvar care techniques (36%). All of then provided only low pain relief. Physical therapy and oral gabapentin provided strong vulvar pain relief. Conclusion Prolonged duration of vulvar pain, multiple visits to healthcare professionals and poor relief of pain are common aspects in the clinical history of women with vulvodynia. Vulvovaginal symptoms other than pain are common, highlighting the importance of the screening tests in order to avoid misdiagnosis.
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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 | 0.008 |
| 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 | 0.000 | 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".