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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".