Suitability of Three Saws for Minimally Invasive Bone Cutting
Bibliographic record
Abstract
This study compares 3 different saw types to determine which is best suited for integration into a minimally invasive bone saw. A handheld electric jigsaw, a coping saw, and a Gigli saw were used to cut into porcine ilium. Heat generated was measured using a thermocouple, and forces applied during cutting were recorded using a force/torque sensor. The coping saw generated an average maximum temperature that was 26 degrees C less than that generated using the jigsaw (P < .001) and 14 degrees C less than that for the Gigli saw (P < .001). On average, the maximum force applied through the coping saw was 14 N less than that through the jigsaw (P < .001) and 18 N less than that through the Gigli saw (P < .001). Out of the 3 saws tested, the coping saw is optimal for cutting bone based on heat generation and required force.
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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".