Prevailing Trends in Haptic Feedback Simulation for Minimally Invasive Surgery
Bibliographic record
Abstract
Background The amount of direct hand-tool-tissue interaction and feedback in minimally invasive surgery varies from being attenuated in laparoscopy to being completely absent in robotic minimally invasive surgery. The role of haptic feedback during surgical skill acquisition and its emphasis in training have been a constant source of controversy. This review discusses the major developments in haptic simulation as they relate to surgical performance and the current research questions that remain unanswered. Search Strategy An in-depth review of the literature was performed using PubMed. Results A total of 198 abstracts were returned based on our search criteria. Three major areas of research were identified, including advancements in 1 of the 4 components of haptic systems, evaluating the effectiveness of haptic integration in simulators, and improvements to haptic feedback in robotic surgery. Conclusions Force feedback is the best method for tissue identification in minimally invasive surgery and haptic feedback provides the greatest benefit to surgical novices in the early stages of their training. New technology has improved our ability to capture, playback and enhance to utility of haptic cues in simulated surgery. Future research should focus on deciphering how haptic training in surgical education can increase performance, safety, and improve training efficiency.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".