Measurement, Analysis, and Display of Haptic Signals During Surgical Cutting
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
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 .
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