A tactile enhancement instrument for minimally invasive surgery
Bibliographic record
Abstract
Objective: During minimally invasive arthroscopy, surgeons use probes as diagnostic tools to detect tissue anomalies. Improving tactile sensitivity during this activity would be valuable.Materials and Methods: We developed an enhanced probe that could enhance the tactile sensations experienced while probing objects. It operated by detecting the acceleration signal resulting from the interaction of the tool tip with surfaces and by magnifying it for tactile and auditory reproduction. The instrument consisted of an accelerometer and an actuator arranged such that the sensing direction was orthogonal to the actuating direction so as to decouple input from output. Using the instrument, subjects were asked to detect cuts under four conditions: with no amplification, with enhanced tactile feedback, with sound feedback, and with passive touch.Results: We found that for tactile reproduction, the current prototype could amplify the signals by 10 dB on average. Results from statistical methods showed significant improvements in performance in the case of tactile and auditory feedbacks.Conclusion: We developed a surgical probe with tactile and auditory feedbacks. Despite the moderate system gain achievable with the initial prototype, the system could measurably improve users' ability to detect small cuts in cartilage-like elastic surfaces.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".