Cone-beam computed tomography on a mobile C-arm: novel intraoperative imaging technology for guidance of head and neck surgery.
Bibliographic record
Abstract
OBJECTIVES: A conventional approach to image-guided surgery relies on positional tracking relative to preoperative images. We investigated the performance of intraoperative cone-beam computed tomography (CBCT) on a mobile C-arm for real-time guidance of head and neck surgery. Objectives were as follows: (1) to quantify improvements in surgical performance achieved with intraoperative CBCT and (2) to investigate specific, challenging surgical tasks for which CBCT is essential for total target ablation and critical structure avoidance. METHODS: Surgical performance was evaluated using a phantom model in which a simulated skull base lesion was excised with and without intraoperative CBCT guidance. Performance was quantified by means of statistical decision theory analysis for conservative and radical excision tasks, yielding measures of sensitivity and specificity for each surgical task. Cadaveric specimens were employed to demonstrate the efficacy of CBCT guidance in sinus and skull base surgery. RESULTS: Performance under CBCT guidance was significantly increased in all cases, particularly for radical excision tasks in proximity to critical normal structures. Cadaver studies demonstrated that CBCT-guided procedures yielded higher-quality surgical product and higher conformity to surgical margins with dramatically increased surgical confidence. CONCLUSIONS: Intraoperative CBCT quantifiably improved surgical performance in all excision tasks and significantly increased surgical confidence. CBCT offers an intraoperative three-dimensional imaging technology that provides exquisite, real-time visualization of sinus and skull base anatomy. Such intraoperative imaging in combination with real-time tracking and navigation should be of great benefit in delicate procedures in which excision must be executed in close proximity to critical structures.
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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".