Periodic Kinesthetic Guidance Cannot Expedite Learning Surgical Skills
Bibliographic record
Abstract
Introduction. Connecting multiple haptic devices in a master-slave fashion enables us to deliver kinesthetic (haptic) feedback from 1 person to another. This study examined whether inter-user feedback delivered from an expert to a novice would facilitate skill acquisition of the novice in learning laparoscopic surgery and expedite it compared to traditional methods. Methods. We recruited fourteen novices and divided them into 1 of 2 training groups with 6 half-hour training sessions. The task was precision cutting adopted from one of the tasks listed in Fundamentals of Laparoscopic Surgery using laparoscopic instruments. In the haptic feedback group (haptic), 8 subjects had the chance to passively feel an expert’s performance before they started to practice in each training session. In the self-learning group (control), 6 subjects watched a video before practicing. Each session was video recorded, and task performance was measured by task completion time, number of grasper adjustments, and instrument crossings. Cutting accuracy, defined as the percentage of deviation of the cutting line from the predefined line, was analyzed via computer analysis. Results. Results show no significant difference among performance measures between the 2 groups. Participants performed similarly when practicing alone or with periodic haptic feedback. Discussion. Further research will be needed for improving our way of integrating between-person haptic feedback with skills training protocol.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".