Bibliographic record
Abstract
RATIONALE: Rhinophyma is a slowly progressive, disfiguring disease of the nose. A variety of surgical techniques have been described for its management, which have had varying success. OBJECTIVE: To describe the technique and results of the senior author (P.B.) using electrocautery decortication and/or CO(2) laser contouring in seven consecutive patients with rhinophyma by retrospective review. DESIGN: A retrospective case series of seven consecutive adult patients treated from 1999 to 2005 inclusive. METHODS: A retrospective chart review was conducted of the seven patients treated at the local tertiary care centre over a 6-year period. RESULTS: In the seven consecutive patients who were operated on by the senior author, all had been previously diagnosed with acne rosacea. Five patients had moderate and two had major rhinophyma. Five patients (71%) had an excellent or very good result using the described technique. CONCLUSION: Electrocautery nasal decortication and/or CO(2) laser contouring in patients with rhinophyma is an effective technique that results in a satisfied patient with an aesthetically suitable nose. Caution should be exercised when vaporizing tissue in the nasal alar region with a CO(2) laser to prevent postoperative alar retraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".