Cone-Beam Computed Tomography-Guided Navigation in Complex Osteotomies Improves Accuracy at All Competence Levels
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess the accuracy and reproducibility of a novel cone-beam computed tomography (CBCT)-guided navigation system designed for osteotomies with joint-sparing bone cuts. METHODS: Eighteen surgeons participated in this study. First, 3 expert tumor surgeons resected bone tumors in 3 Sawbones tumor models identical to actual patient scenarios. They first performed these osteotomies without navigation and then performed them using a navigation system and 3-dimensional (3D) planning tools based on CBCT imaging. The 2 sets of measurements were compared using image-based measurements from post-resection CBCT. Next, 15 residents, fellows, and orthopaedic staff surgeons were instructed on the use of the system, and their navigated resections were compared with navigated resections performed by the 3 expert tumor surgeons. RESULTS: One hundred and twenty-six navigated cuts done by the orthopaedic oncologists were compared with 126 non-navigated cuts by the same surgeons. The cuts violated the tumor in 22% (6) of the 27 non-navigated resections compared with none of the 27 navigated resections. The navigated cuts were significantly more accurate in terms of entry point, pitch, and roll (p < 0.001). The variation among the 3 surgeons when they used navigation was <0.6 mm for the entry cut and, on average, 1.5° for pitch and roll. All 18 surgeons then completed a total of 144 navigated cuts. The level of experience did not result in a significant difference among groups with regard to cut accuracy. Two cuts went into the tumor. The mean distance from the planned bone cuts to the actual entry points into bone was 1.5 mm (standard deviation [SD] = 1.4 mm) for all users. The mean difference in pitch and roll between the planned and actual cuts was 3.5° (SD = 2.8°) and 3.7° (SD = 3.2°) for all users. CONCLUSIONS: Even in expert hands, navigated cuts were significantly more accurate than non-navigated cuts. When the osteotomies were aided by navigation, their accuracy did not differ according to the level of professional experience. CBCT-based metrics enable intraoperative assessments of cut accuracy and reconstruction planning. CLINICAL RELEVANCE: CBCT-guided navigated osteotomies can improve accuracy regardless of surgeon experience and decrease the variability among different surgeons.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".