A System for Laparoscopic Surgery Ergonomics and Skills Evaluation
Bibliographic record
Abstract
This article presents a system for tracking, recording, and analysis of instrument and surgeon's arm motion in minimally invasive surgeries. The captured trajectories can be objectively analyzed for both ergonomic assessment and skills evaluation. The system consists of two special infrared (IR) markers that are used for 6 degrees of freedom (DOF) laparoscopic instrument tracking and a set of 3DOF IR markers attached to elbows and shoulders. A compact IR camera tracks and records the markers during a standardized training task (eg, suturing). The instrument markers were purposely designed to provide good tracking while minimizing their volume. The accuracy of the instrument markers was evaluated showing a root mean square error of 0.61 mm, 1.0 mm, and 2.4 mm at distances from the camera of 0.5 m, 0.68 m, and 1 m respectively. Furthermore, some sample trajectories were recorded during an in-trainer suturing task. The Results section presents the values of basic skills metrics computed from the acquired data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".