A network meta‐analysis on the efficacy and safety of monotherapies for tinea capitis, and an assessment of evidence quality
Bibliographic record
Abstract
Various monotherapies exist for tinea capitis; however, their relative efficacies have never been determined using a statistical approach which compares treatments' efficacy simultaneously. The goal of this study was to determine the relative efficacy (mycologic and complete cure rates) of monotherapies for the treatment of tinea capitis. On October 5, 2019, searches were performed in Scopus, PubMed, EMBASE, MEDLINE (Ovid), and CINAHL; there were no date restrictions. For the main network meta-analysis, eligible studies were randomized trials that investigated the effect of tinea capitis monotherapies on subjects' mycological and complete cure rates. Network meta-analyses were conducted in accordance with the 2015 Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist for network meta-analyses. Mycological cure rate was the primary outcome; complete cure rate and adverse events were secondary outcomes. Twelve studies met the eligibility criteria for the main network; five systemic monotherapies were identified, griseofulvin, ketoconazole, terbinafine, itraconazole, and fluconazole. When the causative species was of the Microsporum genus, griseofulvin was most efficacious in terms of mycological cure (SUCRA = 66.1%) and complete cure (SUCRA = 80.6%). For tinea capitis caused by the Trichophyton species, terbinafine was the most efficacious in terms of both mycological and complete cure (SUCRA values of 75.2% and 78.2%, respectively). Risk of adverse events did not significantly differ across the interventions. Our results are congruent with those of previous pairwise meta-analyses; our findings also corroborate clinical experience and anecdotal evidence.
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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 | 0.051 | 0.112 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.061 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".