Graphical Rendering of Localized Lumps for MIS Applications
Bibliographic record
Abstract
Minimally invasive sugery (MIS) has increasingly been used in different surgical routines despite having significant shortcomings such as a lack of tactile feedback. Restoring this missing tactile information, particularly the information gained through tissue palpation, would be a significant enhancement to MIS capabilities. Tissue palpation is particularly important and commonly used in locating embedded lumps. The present study is inspired by this major limitation of the MIS procedure and is aimed at developing a system to reconstruct the lost palpation capability of surgeons in an effective way. By collecting necessary information on the size and location of hidden features using MIS graspers equipped with tactile sensors, the information can be processed and graphically rendered to the surgeon. Therefore, using the proposed system, surgeons can identify the presence or absence, location, and approximate size of hidden lumps simply by grasping the target organ with a smart endoscopic grasper. The results of the conducted experiments on the prototyped MIS graspers represented by graphical images are compared with those of the finite element models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".