Commentary on: Cosmetic Surgery Training in Canadian Plastic Surgery Residencies: Are We Training Competent Surgeons?
Notice bibliographique
Résumé
In the article titled “Cosmetic Surgery Training in Canadian Plastic Surgery Residencies: Are We Training Competent Surgeons?” the authors discuss the feelings of Canadian plastic surgery residents toward their training in cosmetic surgery and their comfort level with the discipline, as self-reported in a survey. Their results suggested that less than 20% of graduating residents felt adequately prepared to integrate cosmetic surgery into future practice. Furthermore, the level of confidence was lowest with facial and nonsurgical procedures. Just less than half of graduating residents surveyed planned on entering private practice, and their expectation to offer cosmetic surgery as a component of private practice was high. The findings of this study highlight the importance of formal teaching in cosmetic surgery. Currently, most programs have a formal cosmetic surgery rotation, but residents felt their involvement in patient care was less than 20%. Resident cosmetic clinics and elective rotations were thought to enhance education in cosmetic surgery. Assuming a resident/fellow cosmetic surgery clinic model, training residents in cosmetic plastic surgery does present several unique challenges. The surgeon overseeing a cosmetic operation performed by a resident is often not financially compensated for his or her time. Prospective patients are cautious, knowing that a surgeon in training will be performing their elective and self-paid cosmetic procedure. Although offered at a reduced rate, the overhead fee charged to the patient may be a barrier to booking surgery. This may result in a less-than-desirable flow of patients through the clinic. When complications occur, the responsibility ultimately falls on the supervising faculty surgeon. These are just some reasons why resident clinics may not be universally incorporated into resident training programs. The plastic surgery resident training program at the University of Toronto has an established resident rotation that is 4 months in duration and a fellowship program that is 6 months in duration. This is a comprehensive rotation whereby the resident works alongside an aesthetic surgery fellow. We believe the success of such a rotation relies on thorough simulation of an actual practice. This includes exposure as primary surgeon and assistant with simultaneous intraoperative teaching, evaluation of patients through resident-conducted consultations, continuity of patient care with close follow-up, and provision of nonsurgical procedures in a clinic setting. Exposure to the full spectrum of aesthetic surgery, including procedures for the face, breast, and body, is important. This clinical experience dovetails into a seminar series covering the complete spectrum of cosmetic surgery. Although elective rotations offer further exposure to the diversity of cosmetic surgery, often the trainee is left observing with minimal clinical involvement. It is difficult to specify a number of surgical case experiences after which a resident is comfortable with a given cosmetic operation. It is expected that confidence and expertise are gained through each patient interaction and will continue to develop well into independent practice. Our residents and fellows perform approximately 20 to 30 procedures as primary surgeons during their rotation. However, it is more important to recognize that clinical comfort is multifactorial and is also dependent on patient evaluation, follow-up, assisting other surgeons, and small-group topic-based teaching. After 6 months, our fellows (subjectively speaking) feel comfortable incorporating cosmetic surgery into their practice. The article by Chivers et al identifies that improvements are needed in the current teaching of cosmetic surgery in Canadian plastic surgery residency programs. Despite the potential challenges faced when teaching cosmetic surgery, improvement is necessary to maintain leadership in this important component of our specialty. The author declared no potential conflicts of interest with respect to the research, authorship, and publication of this article.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,035 | 0,026 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».