Fully ablative CO2 laser therapy for rhinophyma: long-term efficacy, safety and insights from an artificial intelligence-assisted predictive model in a large cohort
Bibliographic record
Abstract
Abstract Background Rhinophyma, a progressive nasal deformity resulting from advanced rosacea, presents significant cosmetic and functional challenges. Fully ablative CO2 laser therapy is a recognized treatment modality, but data on its long-term efficacy and safety, and recurrence rates remain limited. Objectives To assess the long-term outcomes, safety and patient satisfaction associated with fully ablative CO2 laser therapy for rhinophyma and to identify predictors of treatment success using an artificial intelligence (AI)-assisted model. Methods A retrospective study was conducted on 152 patients with rhinophyma (grades I–III) treated with CO2 laser therapy at an outpatient clinic affiliated with McGill University. Patients were evaluated for aesthetic improvement using the Global Aesthetic Improvement Scale (GAIS), satisfaction surveys and follow-up assessments at 1, 3, 6 and 12 months post-treatment. Demographic and clinical data were analysed using descriptive statistics, logistic regression and a deep learning model to determine predictors of hypopigmentation, recurrence and patient outcomes. Results Significant aesthetic improvement (GAIS ≥ 3) was observed in 84% of p atients, with an average satisfaction score of 2.51/3. Recurrence was rare (4%), occurring primarily in older men with grade III rhinophyma. Side effects included mild hypopigmentation (9%) and textural changes (2%). A deep learning model identified rhinophyma grade, age and Fitzpatrick phototype as key predictors of treatment outcomes. Logistic regression confirmed that advanced rhinophyma grades significantly reduced hypopigmentation risk [adjusted odds ratio (aOR) 0.43, 95% confidence interval (CI) 0.18–0.97; P = 0.041], while age significantly increased it (aOR 1.08, 95% CI 1.00–1.17, P = 0.026). Higher rhinophyma grades were also strongly associated with increased patient satisfaction (aOR 4.97, 95% CI 2.79–9.48; P < 0.001) and showed a trend toward higher recurrence risk (aOR = 6.11, 95% CI: 0.92–690.69, P = 0.064). Fitzpatrick phototype was significantly associated with patient satisfaction; decreasing Fitzpatrick phototype was associated with reduced odds of patient-reported satisfaction (aOR 0.48, 95% CI 0.25–0.87; P = 0.015). Conclusions Fully ablative CO2 laser therapy is highly effective and safe for rhinophyma, with most patients achieving significant improvements after a single session. AI-assisted analysis provides valuable insights into predictors of success, enabling personalized treatment plans. Older patients had an 8% increased risk of hypopigmentation per year of age, while patients with severe rhinophyma were at lower risk of hypopigmentation but at higher risk of recurrence. These findings reinforce the role of CO2 laser therapy as a cornerstone treatment for rhinophyma while highlighting the utility of predictive analytics in guiding treatment strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".