Calibrating surgical SmartForcepsTM using bootstrap and multilevel modeling techniques
Bibliographic record
Abstract
Knowledge of forces, exerted on brain tissues during the performance of neurosurgeries, is critical for quality assurance, rehearsal, and training purposes. Quantifying the interaction forces has been made possible by developing SmartForceps, a bipolar forceps retrofitted by a set of strain gauges. The unknown values of implemented forces are estimated using voltages read from strain gauges. To this end, one needs to quantify the force-voltage relationship to estimate the interaction forces during microsurgery. In this thesis, we employed different probabilistic methodologies such as bootstrapping, weighted least squares regression, Bayesian regression and multi-level modeling in order to estimate the implemented force on tissue using voltages read from strain gauges. We obtain both point and interval estimates of the applied forces at the tool tips and calculate the precision associated with each point estimate. As a proof-of-concept, the proposed techniques were then employed to estimate unknown forces, and construct necessary confidence intervals using observed voltages in data sets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".