State-of-the-Art Review: Managing Vulvovaginal Candidiasis
Bibliographic record
Abstract
Vulvovaginal candidiasis is one of the most prevalent infections in women worldwide. Together with its recurrent form, it affects millions of women annually, causing significant symptoms and severely impacting quality of life. This review examines the pathophysiology, risk factors, microbiome interactions, clinical manifestations, and challenges in diagnosing and managing vulvovaginal candidiasis, with emphasis on recurrent vulvovaginal candidiasis. While Candida albicans is the primary cause, non-albicans species are increasingly common. Multiple factors contribute to both forms, including hormonal changes, diabetes, antibiotic use, immune dysfunction, and genetics. The vaginal microbiome plays a key role in maintaining homeostasis and preventing Candida overgrowth. Symptoms such as itching, discharge, and soreness overlap with other conditions, complicating the diagnosis. Standard treatment involves topical or systemic antifungals, but recurrence and resistance are frequent. Emerging strategies include novel antifungals, immunomodulators, and vaccines. Future approaches should focus on modulating host and environmental factors to prevent recurrence, reduce resistance, and improve outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".