Real-time measurement of tool-tissue interaction forces in neurosurgery: Quantification and analysis
Bibliographic record
Abstract
Understanding the amount of forces exerted to the brain tissue during the performance of surgical tasks in neurosurgery is critical for educating trainees. Quantifying such forces can help trainees gain important information about the appropriate amount of force required to safely, yet effectively, complete microsurgical tasks. This paper reports the amount of forces exerted during the performance of neurosurgical tasks by means of a force-sensing bipolar forceps, retrofitted by a set of force sensing components. An experienced surgeon and a surgical team conducted a variety of microsurgical tasks on a cadaver brain using the developed instrumented bipolar forceps, while the forces of dissections were measured real-time. Results showed that depending on the surgical task, the peak (effective) value of dissection forces varied between 0.50 N and 1.84 N. Correlation between calculated force signals, during performance of different trials for the same task was investigated using cross correlation test. Results indicated a strong link between the forces measured in different trials.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".