LIVE SURGERY BROADCAST USING MIXED REALITY: WHAT ARE THE ADVANTAGES AND CHALLENGES?
Bibliographic record
Abstract
Live surgery broadcast is an innovative method of introducing new operative techniques to surgeons around the world. However, due to the potential impact on patient outcomes, its use remains somewhat controversial. The objective of this study was to determine the effect of live surgery on operative time and complication rate in patients undergoing AAGR for shoulder instability. We hypothesized that live surgery would maintain a similar surgical time and intra- and post-operative complication profile. This was a retrospective review of 94 patients who underwent AAGR between 2013-2023. A 1:1 ratio was used to match patients who had AAGR during a live surgery demonstration (Broadcast group) to patients who underwent AAGR without live surgery (No Broadcast group), based on sex, BMI (+ 3), and age (+ 3). The primary outcome for the study was surgical time, defined as the time from initial incision to final closure. Secondarily, we compared the intra-operative and early post-operative complications (< 1 month) between the groups. The Broadcast group had a significantly greater procedure time compared to the No Broadcast group (86.90 + 13.1 vs. 80.70 +14.0 minutes, p 0.0.5). One patient (2.4%) in the Broadcast group developed a post-operative hematoma, while one patient (2.4%) in the No Broadcast group developed a wound infection. This study demonstrated that live surgeries are on average 6 minutes longer but are not associated with increased complications in patients undergoing AAGR.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".