Sexually Transmitted Dermatophyte Infections—A Scoping Review
Bibliographic record
Abstract
Sexually transmitted dermatophyte infections are an emerging public health concern, with increasing incidence reported across multiple countries. These infections are mainly spread through direct skin-to-skin contact during sexual activity and are more commonly found in individuals with high-risk sexual practices. The likelihood of infection is heightened by frequent pubic hair grooming or regular use of shared spaces like gyms and saunas. Clinically, presentations are often severe, widespread and atypical, which may delay diagnosis or lead to misidentification. Accurate species-level identification is critical and increasingly reliant on molecular sequencing techniques, including ITS and tef1α regions, which are also valuable for strain surveillance and contact tracing. Management strategies should emphasise systemic antifungal therapy, with consideration for adjunctive topical agents or antibiotics in cases of secondary infection. Individualised treatment plans may require extended therapy durations or combination regimens to ensure clinical resolution. In addition to pharmacologic intervention, education on hygiene practices, risk of reinfection and the importance of environmental decontamination and follow-up care is essential for preventing recurrence and curbing transmission.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".