Vestibulophyma and Giant Rhinophyma Associated With Variant Rosacea
Bibliographic record
Abstract
In July 2013, a 44-year-old Taiwanese man was admitted to Kaohsiung Chung-Ho Memorial Hospital, presenting with a 3-year history of progressively growing nasal mass with pustules and foul smelling discharge. He is a victim of cerebrovascular accident 4 years ago, hypertension for 12 years, and left hemiplegia. He has no personal history of smoking or alcoholism. Facial disfigurement led to psychological distress, and he sought out residence at a nursing home as a social recluse. Examination revealed a giant pedunculated bulbar mass measuring 23 × 13 × 8 cm and extending from the forehead to over the tip of the nose, with complete obliteration of the normal nasal contour. MRI scan revealed extensive nasal cutaneous lobulated tumors with complete obstruction of his left nostril due to vestibular phymatous lesions. Following biopsy results, we diagnosed the patient with “vestibulophyma”, giant rhinophyma with extensive external phymatous lesions, and concomitant presentation of all four rosacea subtypes. After undergoing application of topical metranidazole jelly and low oral dose of doxycycline (40 mg/day) for 2 weeks, we proceeded with surgical intervention of giant rhinophyma de-bulking, vestibulophyma resection, inferior turbinectomy, and middle turbinectomy with microdebrider. Full thickness skin graft played an important role as an ideal and effective biological dressing during the healing process. The patient is currently well and satisfied with the results. J Med Cases. 2015;6(5):216-218 doi: http://dx.doi.org/10.14740/jmc2121w
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".