Measurement, Analysis, and Display of Haptic Signals During Surgical Cutting
Bibliographic record
Abstract
The forces experienced while surgically cutting anatomical tissues from a sheep and two rats were investigated for three scissor types. Data were collected in situ using instrumented Mayo, Metzenbaum, and Iris scissors immediately after death to minimize postmortem effects. The force-position relationship, the frequency components present in the signal, the significance of the cutting rate, and other invariant properties were investigated after segmentation of the data into distinct task phases. Measurements were found to be independent of the cutting speed for Mayo and Metzenbaum scissors, but the results for Iris scissors were inconclusive. Sensitivity to cutting tissues longitudinally or transversely depended on both the tissue and on the scissor type. Data from cutting three tissues (rat skin, liver, and tendon) with Metzenbaum scissors as well as blank runs were processed and displayed as haptic recordings through a custom-designed haptic interface. Experiments demonstrated that human subjects could identify tissues with similar accuracy when performing a real or simulated cutting task. The use of haptic recordings to generate the simulations was simple and efficient, but it lacked flexibility because only the information obtained during data acquisition could be displayed. Future experiments should account for the user grip, tissue thickness, tissue moisture content, hand orientation, and innate scissor dynamics. A database of the collected signals has been created on the Internet for public use at www.cim.mcgill.ca/∼haptic/tissue/data.html .
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".