A novel tactile probe with medical and surgical applications
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
Purpose The paper aims to discuss design, fabrication, testing and simulation of a novel tactile probe used for measuring the stiffness of biological soft tissues/materials with a view to medical and surgical applications. Design/methodology/approach Both finite element modeling and experimental approach were used in this research. The novel tactile probe capable of recording force-deformation feedback is accompanied with the tactile-status-display which is a custom-designed user-friendly interface. This system can evaluate the stiffness in each part of force-deformation status. Findings The new system named novel tactile probe was fabricated, and the results on artificial materials (with different stiffnesses) and the sheep kidney (containing a hard object) were reported. Recording different stiffnesses, detecting hard object embedded in soft tissue and predicting the exact location of it are the main results that have been extracted through the diagrams obtained by the novel tactile probe system. Research limitations/implications The designed and fabricated system can be modified and miniaturized to be used during different minimally invasive surgeries in the future. Practical implications The most distinguishing feature of this novel tactile probe is its applicability during different laparoscopic surgeries, so the in vivo data can be obtained. Originality/value For the first time, a tactile probe has been designed and tested in the form of laparoscopic instrument which upgrades the efficiency of available laparoscopic instruments. Also, the novel tactile probe can be used in both in vivo and in vitro experimental setups for measuring the stiffness of sensed objects.
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 | 0.001 |
| 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 it