Tool-Tissue Interaction Forces in Glioma Surgery
Bibliographic record
Abstract
Objectives: Knowledge and understanding of optimal forces required for tissue handling in neurosurgery are fundamental to accomplishing the task effectively, and safely. Surgical simulation has shown that >50% of errors made by surgical trainees are due to the inappropriate use of force. Neurosurgical training in tool-tissue interaction force is predominantly taught through an apprenticeship model, in which experts supervise trainees and provide qualitative and subjective feedback, such as “be gentle” or “retract more.” Furthermore, in glioma surgery, the emphasis has remained on extent of resection; forces of surgical dissection, while emerging concept, have not been studied. Here, we define the forces in Newton (N), observed for different surgical tasks in glioma surgery comparing the force profiles of trainees to expert surgeons. Methods: “SmartForceps System,” a force-sensing bipolar forceps was developed to quantify tool-tissue forces during surgery. Eleven surgeons (three groups: novice, intermediate, and expert) participated in the study, with expert surgeons ( n = 2, 10+ years of experience), novice surgeons ( n = 3, PGY 1–3), and intermediate surgeons ( n = 6, PGY 4–6 including clinical fellowship). Fourteen patients who underwent surgical resection of glioma using the SmartForceps System between November 2019 and July 2021 were included. We recorded the force profile of five predetermined surgical tasks: (1) dissection, (2) coagulation, (3) retracting, (4) pulling, and (5) manipulating. Task-specific force recording started from the time when the surgeon stated the specific task, and stopped when the forceps tip was no longer in contact with the tissue. The force profile was measured for each trial and included force duration, average, maximum, minimum, range, standard deviation, and correlation coefficient. Results: Fourteen patients (8 males and 6 females; mean [SD] age = 55 [16] years) underwent 16 surgeries, with histopathological diagnosis of glioblastoma multiforme (GBM, n = 9), anaplastic astrocytoma ( n = 2), oligodendroglioma ( n = 2), and astrocytoma ( n = 1). Force data from 1,206 trials were collected of which 846 trials were recorded for expert surgeons. The mean (SD) for tumor coagulation was 0.32 ± 0.24 N. The forces exerted by novice surgeons were significantly lower than those of expert and intermediate surgeons (0.21 vs. 0.33 and 0.33; p = 0.002). There was no difference in the force profiles between intermediate and expert surgeons. Force variability decreased from novice (0.90) to intermediate (0.81) to expert (0.66) surgeons. Of all the tasks, coagulation required the least amount of force but this was only significantly lower than manipulation (0.32 vs. 0.47; p < 0.0001). Oligodendroglioma required lower coagulation force than astrocytic tumors (0.19 vs. 0.34; p < 0.0001). Conclusion: Novice surgeons exert lower forces during glioma surgery. This finding may suggest uncertainty and difficulty in differentiating tumor from normal brain. Force variability during glioma surgery decreased with experience. The quantification of tool-tissue interaction forces during surgery and knowledge of such through force display and report accessible through secure portal and applications, may enhance the learning and safety of surgery. Fig. 1 Fig. 2 Publication History Article published online: 15 February 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".