Tool-Tissue Interaction Forces in Glioma Surgery
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».