Intraoperative cone‐beam CT‐guided osteotomy navigation in mandible and maxilla surgery
Bibliographic record
Abstract
OBJECTIVES: Surgical navigation systems based on preoperative imaging are now increasingly used for guidance of head and neck resection and reconstruction. The primary aim of this study was to quantify osteotomy cutting accuracy using an image-guidance system for intraoperative cone-beam computed tomography (CBCT) imaging and surgical saw navigation. To enable clinical translation of this CBCT-guided navigation system, a secondary aim of the study was to design and fabricate a patient reference tracker suitable for clinical use on a mobile mandible. METHODS: First, a preclinical cadaveric study was performed to quantify navigation accuracy with the use of clinically suitable patient reference trackers. Second, a proof-of-principle patient study was conducted to evaluate this technique under clinical conditions. RESULTS: In both preclinical (5 cadavers) and clinical (5 patients) experiments, the mean cutting accuracy was less than 2 mm. In all preclinical specimens, bilateral mandibulectomies and bilateral maxillectomies were performed, for a total of 20 cut planes for analysis. The mean (standard deviation [SD]) values for distance, pitch, and roll were 1.4 mm (1.1 mm), 4.2° (3.5°), and 2.9° (2.5°) mm, respectively. Five mandibulectomies were performed on five patients, for a total of 10 cut planes for analysis. The mean (SD) values for distance, pitch, and roll were 1.7 mm (0.8 mm), 5.4° (1.5°), and 6.7° (4.6°) mm, respectively. CONCLUSIONS: The overall performance in comparison to alternative approaches warrants further consideration. In terms of accuracy, the results presented here are comparable to recent systematic reviews assessing CAD-CAM cutting guides that cite accuracies of ~2 to 2.5 mm. LEVEL OF EVIDENCE: 2 Laryngoscope, 130:1122-1127, 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".