Enhancing skill learning with dual-user haptic feedback: insights from a task-specific approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: This study was to examine whether inter-user haptic feedback would have a differential impact on skill acquisition based on the nature of the surgical task involved. Specifically, we hypothesized that haptic feedback would facilitate target orientation more than cutting tasks in the context of laparoscopic surgery. Methods: Ten novice participants were recruited and assigned to one of two training groups. Each group underwent six half-hour training sessions dedicated to laparoscopic pattern-cutting tasks. In the haptic group, five participants received expert guidance during the training sessions, whereas the remaining five participants in the control group engaged in self-practice. All trials were recorded on video, enabling a comparative analysis of task performance between the participants’ left hand (target manipulation) and right hand (cutting task). Additionally, the number of haptic feedback instances provided to the trainees in the haptic group was recorded. Results: Practice led to a reduction in total task time, grasping time, and cutting errors. However, no significant differences were observed between the two training groups, except for the grasping time, where haptic feedback significantly reduced the grasping time compared to the control group. Moreover, the frequency of haptic feedback instances provided to the trainees was notably higher for the grasping than for the cutting task. Discussion: Our study suggests that haptic feedback has a more substantial impact on orientation tasks than on cutting tasks in laparoscopic surgery training. However, we acknowledge that a larger sample size would provide a more robust evaluation of this effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it