Diagnosis and Management of Cutaneous Tinea Infections
Bibliographic record
Abstract
GENERAL PURPOSE: To provide information about the epidemiology, clinical features, and management of cutaneous tinea infections. TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After completing this continuing education activity, you should be better able to:1. Summarize the epidemiology related to cutaneous tinea infections.2. Describe the clinical features of cutaneous tinea infections.3. Identify features related to the diagnosis and management of cutaneous tinea infections. ABSTRACT: Dermatophyte or tinea infection refers to a group of superficial fungal infections of the hair, skin, and nails. Tinea infections are most commonly caused by fungi of the genus Trichophyton, Microsporum, or Epidermophyton. Cutaneous manifestations of tinea infections are seen worldwide and classified based on the affected body site. The diagnosis of these conditions is complicated by morphologic variations in presentation and overlap with other common infectious and noninfectious entities. As a result, diagnosis and appropriate management of these conditions are essential to avoid patient morbidity. This case-based review summarizes the epidemiology, relevant clinical features, microbiology, and management considerations for commonly encountered tinea infections.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".