Computer-assisted planning and navigation improves cutting accuracy during simulated bone tumor surgery of the pelvis
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Resection of bone tumors within the pelvis requires good cutting accuracy to achieve satisfactory safe margins. Manually controlled bone cutting can result in serious errors, especially due to the complex three-dimensional geometry, limited visibility, and restricted working space of the pelvic bone. This experimental study investigated cutting accuracy during navigated and non-navigated simulated bone tumor cutting in the pelvis. METHODS: A periacetabular tumor resection was simulated using a pelvic bone model. Twenty-three operators (10 senior and 13 junior surgeons) were asked to perform the tumor cutting, initially according to a freehand procedure and later with the aid of a navigation system. Before cutting, each operator used preoperative planning software to define four target planes around the tumor with a 10-mm desired safe margin. After cutting, the location and flatness of the cut planes were measured, as well as the achieved surgical margins and the time required for each cutting procedure. RESULTS: The location of the cut planes with respect to the target planes was significantly improved by using the navigated cutting procedure, averaging 2.8 mm as compared to 11.2 mm for the freehand cutting procedure (p < 0.001). There was no intralesional tumor cutting when using the navigation system. The maximum difference between the achieved margins and the 10-mm desired safe margin was 6.5 mm with the navigated cutting process (compared to 13 mm with the freehand cutting process). CONCLUSIONS: Cutting accuracy during simulated bone cuts of the pelvis can be significantly improved by using a freehand process assisted by a navigation system. When fully validated with complementary in vivo studies, the planning and navigation-guided technologies that have been developed for the present study may improve bone cutting accuracy during pelvic tumor resection by providing clinically acceptable margins.
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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) | 0.001 | 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 it