Navigated Pelvic Osteotomy and Tumor Resection
Bibliographic record
Abstract
BACKGROUND: This Sawbones and cadaver study was performed to assess the accuracy and reproducibility of pelvic bone cuts made with use of a novel navigation system with a navigated osteotome and oscillating saw. METHODS: Using a novel navigation system and a three-dimensional planning tool, we navigated pelvic bone cuts that were representative of typical cuts made in pelvic tumor resections. The system includes a prototype mobile C-arm for intraoperative cone-beam computed tomography, real-time optical tracking (Polaris), and three-dimensional visualization software. Three-dimensional virtual radiographs were utilized in addition to triplanar (axial, sagittal, and coronal) navigation. In part one of the study, we navigated twenty-four sacral bone cuts in Sawbones models and validated our results in sixteen similar cuts in cadavers. In part two, we developed three Sawbones models of pelvic tumors based on actual patient scenarios and compared three navigated resections with three non-navigated resections for each tumor model. Part three assessed the accuracy of the system with multiple users. RESULTS: There were ninety navigated cuts in Sawbones that were compared with fifty-four non-navigated cuts. In the navigated Sawbones cuts, the mean entry and exit cuts were 1.4 ± 1 mm and 1.9 ± 1.2 mm from the planned cuts, respectively. In comparison, the entry and exit cuts in Sawbones that were not navigated were 2.8 ± 4.9 mm and 3.5 ± 4.6 mm away from the planned osteotomy site. The navigated cuts were significantly more accurate (p ≤ 0.01). In the cadaver study, navigated entry and exit cuts were 1.5 ± 0.9 mm and 2.1 ± 1.5 mm from the planned cuts. The variation among three different users was 1 mm on both the entry and exit cuts. CONCLUSIONS: Navigation to guide pelvic bone cuts is accurate and feasible. Three-dimensional radiographs should be used for improved accuracy. Navigated cuts were significantly more accurate than non-navigated cuts were. A margin of 5 mm between the target tumor volume and the planned cut plane would result in a negative margin resection in more than 95% of the cuts. CLINICAL RELEVANCE: The accuracy of pelvic bone tumor resections and pelvic osteotomies can be improved with navigation to within 5 mm of the planned cut.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".