Terbinafine‐induced generalized pustular psoriasis (GPP) of von Zumbusch simulating toxic epidermal necrolysis (TEN)
Bibliographic record
Abstract
A 69-year-old man with a history of post-angioplasty myocardial infarction, hypertension, and chronic renal dysfunction presented with a five-day history of progressive erythema that began over the chest before becoming generalized and involving most of the skin surface.Two days prior, he also reported facial swelling, cutaneous pustules, generalized skin sloughing, along with progressive skin tenderness.In addition, he complained of mild oral pain, fatigue, and malaise.Fifteen days prior to the onset of symptoms, his family physician prescribed him a three-week course of terbinafine 250 mg daily for an itchy groin rash.Three days before the onset of symptoms, he was started on a five-day course of prednisone 50 mg daily for a syncopal episode at the emergency department.His other longterm medications included ezetimibe, clopidogrel, aspirin, and metoprolol.In addition to his past medical history, he reported chronic erythematous, thick scaly plaques over his elbows for the past four years.On examination, he presented with an extensive eruption (Figure 1a-c).Notably, Nikolsky's sign was positive revealing areas of skin detachment (Figure 1d,e).The Asboe-Hansen sign was also positive.The total area of skin detachment was over 30% of the body surface area.He also had mucosal erosions of his hard palate.Further examination revealed diffuse nail pitting of his fingernails (Figure 1f).Lab work demonstrated significant neutrophilia of 22.1 × 10 9 /l (4.0-10.0)with toxic granules and vacuoles on microscopy, elevated C reactive protein of 84.1 mg/l (< 10.0), elevated creatinine of 550 µmol/l (58-110), and mild hypocalcemia of 2.09 mmol/ml (2.10-2.55).Streptolysin O antibody, autoimmune serologies, urinalysis, and
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".