Bibliographic record
Abstract
Background: Pityriasis versicolor (PV), also known as tinea versicolor, is caused by Malassezia species. This condition is one of the most common superficial fungal infections worldwide, particularly in tropical climates. PV is difficult to cure and the chances for relapse or recurrent infections are high due to the presence of Malassezia in the normal skin flora. This review focuses on the clinical evidence supporting the efficacy of antifungal treatment for PV. Method: A systematic review of literature from the PubMed database was conducted up to 30 September 2014. The search criteria were “(pityriasis versicolor OR tinea versicolor) AND treatment”, with full text available and English language required. Conclusions: Topical antifungal medications are the first-line treatment for PV, including zinc pyrithione, ketoconazole, and terbinafine. In cases of severe or recalcitrant PV, the oral antifungal medications itraconazole and fluconazole may be more appropriate, with pramiconazole a possible future option. Oral terbinafine is not effective in treating PV and oral ketoconazole should no longer be prescribed. Maintenance, or prophylactic, therapy may be useful in preventing recurrent infection; however, at this time, there is limited research evaluating the efficacy of prophylactic antifungal treatment.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".