MOTION ANALYSIS AS A TOOL FOR THE EVALUATION OF OCULOPLASTIC SURGICAL SKILL
Bibliographic record
Abstract
OBJECTIVE: To evaluate motion analysis as a discriminator of ophthalmic plastic surgical skill between surgeons of varying experience. METHODS: Thirty subjects were divided into 3 groups based on surgical experience: novice (< 5 performed procedures; n = 10), intermediate (5-100 procedures; n = 10), and expert (> 100 procedures; n = 10). Detailed 3-dimensional motion data from surgeons performing 2 oculoplastic surgical tasks on a wet laboratory skills board were obtained using the Qualisys motion capture system. The first task was a deep 3-1-1 suture. The second was skin closure with a continuous suture. The main outcome measures were time, overall path length, and total number of movements. Kruskal-Wallis analysis was performed to evaluate statistical significance. RESULTS: Highly significant differences were found during the skin closure task between all groups for mean time (P = .002), overall path length (P = .002), and number of movements (P = .001). For the deep stitch, highly significant differences were also found for time (P < .001), path length (P < .001), and number of movements (P < .001). CONCLUSIONS: Motion analysis, using this technology, was able to differentiate between surgeons of varying experience performing oculoplastic tasks, thus demonstrating construct validity. This technique may be useful in the objective quantitative measurement of oculoplastic skill, with potential applications for training and research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".