Video-Based Assessment of Surgical Skill in Orthopaedic Surgery
Bibliographic record
Abstract
Introduction: Surgical skills are critical to assess in residency programs. These observations often occur in the clinical settings, which are limited by patient safety and potential bias. High fidelity simulated cadaveric surgery can account for some of these shortcomings. Professional video offers a promising avenue to both anonymize and effectively evaluate surgical skill. The objective of this study were to describe the technique for professional video capture of simulated, open orthopaedic surgeries and to assess construct validity by comparing objective performance scores from the videos with the learner's stage of training. Methods: In 2022, one experienced surgeon and 3 trainees (post graduate year [PGY]-4, PGY-3, PGY-2) were recruited from a residency program to perform 2 moderately challenging surgeries (open reduction and internal fixation of both bone forearm and talus fractures), with fractures simulated using an osteotome. Videographers positioned cameras at various positions throughout a skills laboratory. Total costs were calculated. Statistical analysis was performed to compare evaluator scores of participants' actual level of training. Results: The simulated surgeries were recorded, edited for optimal viewing angles, and anonymized by blurring faces and voice over technology. Seventeen local teaching faculty were recruited to evaluate the videos. The videos were shortened on average 65 minutes for critical steps to be represented in the final production (i.e., Bone reduction, dissection of neurovascular structures, radiographic images, etc.) The full cost to produce the 8 surgical videos was $48,934.00 Canadian dollars. The final data set had 61 observations, with a range of 13 to 17 observations per participant. There was a 19.7% error rate, meaning the videos were generally 80% accurate in predicting the year of training. Conclusions: The discriminative ability of the videos was better at detecting true "novice" and "expert" surgeons but less accurate between the middle years of training. A larger, multicentered study with more participants is needed to draw any further conclusions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".