Quantitative measures of performance in microvascular anastomoses
Bibliographic record
Abstract
OBJECTIVE: Methods of evaluating surgical performance are mainly subjective. This study introduces a method of evaluating surgical performance using a quantitative analysis of tool tip kinematics. METHODS: One experienced surgeon performed eight rat microvascular anastomoses over a 2-day interval. An optoelectronic motion analysis system acquired tool tip trajectories at frequencies of 30 Hz. On the basis of a hierarchical decomposition, the procedure was segmented into specific surgical subtasks (free space movement, needle placement and knot throws) from which characteristic measures of performance (tool tip trajectory, excursion and velocity) were evaluated. Comparisons of performance measures across each procedure were indexed (D scale) using the Kolmogorov-Smirnov statistic. RESULTS: Despite the marker occlusions, tool tip data were obtained 92 +/- 7% (mean +/- SD) of the time during manipulation tasks. Missing data segments were interpolated across gaps of less than 10 sample points with errors less than 0.4 mm. The anastomoses were completed in 27 +/- 4 min (range 20.5-31.4 min) with 100% patency. Tool tip trajectories and excursions were comparable for each hand, while right and left hand differences were found for velocity. Performance measures comparisons across each procedure established the benchmark for an experienced surgeon. The D-scale range was between 0 and 0.5. CONCLUSION: The study establishes a reproducible method of quantitating surgical performance. This may enhance assessment of surgical trainees at various levels of training.
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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 | 0.006 |
| 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 | 0.000 |
| 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".