USING A VIRTUAL MEETING PLATFORM WITH DIRECT SURGEON INTERACTION IS AN EFFECTIVE TOOL FOR SURGICAL EDUCATION: A REMOTE AUGMENTED PRECEPTORSHIP
Notice bibliographique
Résumé
Innovative surgical techniques are being pioneered across the world but teaching these techniques to surgeons poses a logistical challenge, particularly during and after the COVID-19 pandemic. One technique, Arthroscopic Anatomic Glenoid Reconstruction (AAGR), has garnered attention for its low rate of recurrent shoulder dislocations and complications, high levels of patient satisfaction, lower nerve injury rates and avoidance of splitting the subscapularis tendon. A live remote augmented preceptorship (RAP) model has been developed to mitigate logistical challenges associated with in-person learning such as travel, financial constraints, as well as training and technical feasibility. The surgeon (learner) will connect virtually to the expert surgeon (teacher) in the operating room with the use of virtual meeting software, multiple camera angles, and augmented reality. During the live remote preceptorships, the learning surgeon virtually interacts with the teaching surgeon before, during, and after the operation. This study aims to determine surgeon perception and satisfaction of the virtual preceptorship (i.e., RAP model) to learn the AAGR technique. Forty-four trained arthroscopic shoulder surgeons (learners), across two continents (North America & Asia) participated in AAGR preceptorships with the primary investigator (teacher). Before the preceptorship, learners reported approximately how many patients they treat per year with recurrent anterior shoulder instability, their confidence in performing an AAGR, and what they hope to learn from the preceptorship. After the broadcast, learners responded to six questions related to their satisfaction and comfort with various aspects following the broadcast using a 5-point Likert scale. Possible responses ranged from 1 (not satisfied/comfortable) to 5 (very satisfied/comfortable). Before the preceptorship, learners reported various experience with number of patients treated per year with anterior shoulder instability. Most learners reported treating 15 to 30 patients, or more than 50 patients/year who have recurrent anterior shoulder instability. All (100%) learners reported being very satisfied with the overall broadcast, camera angles, content of the live surgery, and the teaching surgeon's ability to answer questions and demonstrate techniques. Regarding the video quality, 80% of learners were very satisfied, and 20% of learners were satisfied. Learners also reported a range of comfortability in performing AAGR pre-preceptorship (22.2%, 30.6%, 22.2%, 13.9%, and 11.1% respectively on scale 1-5). Post-preceptorship, most learners increased their comfortability rating in performing AAGR (3.4%, 6.9%, 24.1%, 37.9%, and 27.6% respectively on a scale from 1-5). Virtual teaching with the use of multiple camera angles, real-time visual/audio feedback, augmented reality, and virtual meeting software is an effective method of surgical education. This method of teaching allows surgeons to connect virtually from anywhere in the world to effectively learn a surgical technique in real-time. Further objective studies on the number of cases performed, surgical time, and patient clinical outcomes by the surgeons (learners) are needed to evaluate the efficiency of this teaching method further.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».