Analysis of Surgical Forces Required to Gain Access Using a Probe for Minimally Invasive Spine Surgery via Cadaveric-Based Experiments Towards Use in Training Simulators
Bibliographic record
Abstract
Introduction: Virtual Reality haptic-based surgical simulators for training purposes have recently been receiving increased traction within the medical field. However, its future adoption is contingent on the accuracy and reliability of the haptic feedback. Goal: This study describes and analyzes the implementation of a set of haptic-tailored experiments to extract the force feedback of a medical probe used in minimally invasive spinal lumbar interbody fusion surgeries. Methods: Experiments to extract linear, lateral and rotational insertion, relaxation and extraction of the tool within the spinal muscles, intervertebral discs and lumbar nerve on two cadaveric torsos were conducted. Results: Notably, mean force-displacement and torque-angular displacement curves describing the different tool-tissue responses were reported with a maximum force of 6.87 (±1.79) N at 40 mm in the muscle and an initial rupture force through the Annulus Fibrosis of 20.550 (±7.841) N at 6.441 mm in the L4/L5 disc. Conclusion: The analysis showed that increasing the velocity of the probe slightly reduced and delayed depth of the muscle punctures but significantly lowered the force reduction due to relaxation. Decreasing probe depth resulted with a reduction to the force relaxation drop. However, varying the puncturing angle of attack resulted with a significant effect on increasing force intensities. Finally, not resecting the thoracolumbar fascia prior to puncturing the muscle resulted with a significant increase in the force intensities. Significance: These results present a complete characterization of the input required for probe access for spinal surgeries to provide an accurate haptic response in training simulators.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".