Fully ablative CO2 laser therapy for rhinophyma: long-term efficacy, safety and insights from an artificial intelligence-assisted predictive model in a large cohort
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».