Cheilitis: A Diagnostic Algorithm and Review of Underlying Etiologies
Bibliographic record
Abstract
Abstract: Cheilitis, or inflammation of the lips, is a common reason for dermatologic consultation. The inflammation can include the vermillion lip, vermillion border, and surrounding skin, and can present with an acute or chronic course. There are many etiologies, including irritant and allergic contact dermatitis, atopic cheilitis, actinic cheilitis, infectious etiologies, nutritional deficiencies, drug-induced cheilitis, and rare etiologies, including granulomatous cheilitis, cheilitis glandularis, plasma cell cheilitis, lupus cheilitis, and exfoliative cheilitis. Distinguishing among the various etiologies of cheilitis is clinically difficult, as many causes may produce similar erythema and superficial desquamation of mucosal skin. In addition, patients report dryness, redness, irritation, burning, fissuring, and itch in many of the underlying causes. Thus, the specific etiology of cheilitis is often difficult to diagnose, requiring extensive testing and treatment trials. In this review, we summarize the various types of cheilitis, synthesizing novel cases, clinical presentations, histopathology, epidemiology, and advancements in diagnostic methods and therapeutics. We provide a diagnostic algorithm aimed to assist clinicians in the management of cheilitis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".