Biopsies of Facial Dermatoses Made Simple
Bibliographic record
Abstract
CONTEXT: Biopsy of the face is rarely done for inflammatory skin diseases, unless the entire process is confined to the face. OBJECTIVE: We hypothesized that facial dermatitis has a differential diagnosis that is more limited than the differential diagnosis of inflammatory skin diseases that affect other parts of the body. To our knowledge, the classification of inflammatory skin diseases occurring on the face has never been conducted before in the English literature. DESIGN: The most-recent 100 facial biopsies of inflammatory skin conditions were retrieved from our files, and the cases were categorized into the main inflammatory skin patterns. RESULTS: Forty-seven cases (47%) were categorized as interface dermatitis, 2 cases (2%) as psoriasiform dermatitis, 11 cases (11%) as spongiotic dermatitis, 16 cases (16%) as diffuse and nodular dermatitis, 8 cases (8%) as perivascular dermatitis, 14 cases (14%) as folliculitis and perifolliculitis, 1 case (1%) as panniculitis, and 1 case (1%) as fibrosing dermatitis. The number of diagnostic entities represented within each of these patterns was small. CONCLUSIONS: We believe that facial dermatitis should have its own more-circumscribed differential diagnosis. From a practical viewpoint, many of the inflammatory skin diseases that affect other parts of the body should be excluded from the differential diagnosis after the tissue is determined to be from a facial skin biopsy, and others should not be considered unless the biopsy is from the face.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".