Vacuum curette lumbar discectomy mechanics for use in spine surgical training simulators
Bibliographic record
Abstract
Simulation in surgical training is a growing field and this study aims to understand the force and torque experienced during lumbar spine surgery to design simulator haptic feedback. It was hypothesized that force and torque would differ among lumbar spine levels and the amount of tissue removed by ≥ 7%, which would be detectable to a user. Force and torque profiles were measured during vacuum curette insertion and torsion, respectively, in multiple spinal levels on two cadavers. Multiple tests per level were performed. Linear and torsional resistances of 2.1 ± 1.6 N/mm and 5.6 ± 4.3 N mm/°, respectively, were quantified. Statistically significant differences were found in linear and torsional resistances between all passes through disc tissue (both p = 0.001). Tool depth (p < 0.001) and lumbar level (p < 0.001) impacted torsional resistance while tool speed affected linear resistance (p = 0.022). Average differences in these statistically significant comparisons were ≥ 7% and therefore detectable to a surgeon. The aforementioned factors should be considered when developing haptic force and torque feedback, as they will add to the simulated lumbar discectomy realism. These data can additionally be used inform next generation tool design. Advances in training and tools may help improve future surgeon training.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".