USING A VIRTUAL MEETING PLATFORM WITH DIRECT SURGEON INTERACTION IS AN EFFECTIVE TOOL FOR SURGICAL EDUCATION: A REMOTE AUGMENTED PRECEPTORSHIP
Bibliographic record
Abstract
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".