A Practical Algorithm for the Management of Superficial Folliculitis of the Scalp: 10 Years of Clinical and Dermoscopy Experience
Bibliographic record
Abstract
INTRODUCTION: Superficial folliculitis of the scalp (SFS) is a common complaint in clinical practice, and initial presentation may be difficult to differentiate as they may appear very similar to each other. OBJECTIVES: The aim of this thesis is to describe the pathologies that occur clinically as folliculitis of the scalp, identify their causes and characteristics and create a standardized classification. METHODS: This is a retrospective clinical, dermoscopic and histopathological study over 10 years of dermatologic consultations. Only individuals with a confirmed diagnosis of SFS (updated diagnostic criteria or biopsy) were included. RESULTS: In this review, we describe the various clinical features of different causes of SFS in ninety-nine cases and divided into infectious due to fungus, bacteria, or virus and inflammatory conditions such as rosacea, acneiform eruption and Ofuji syndrome. CONCLUSIONS: The clinician must differentiate SFS from other underlying scarring disorders to prevent poorer outcomes. We created an algorithm to help the clinician reach a proper diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".