Intraoperative cone‐beam CT for head and neck surgery: Feasibility of clinical implementation using a prototype mobile C‐arm
Bibliographic record
Abstract
BACKGROUND: Intraoperative 3-dimensional (3D) imaging in head and neck surgery was developed using a prototype mobile C-arm for cone-beam CT (CBCT). This article summarizes its implementation in a prospective pilot and feasibility study. METHODS: The CBCT C-arm was used in 12 head and neck surgical oncology cases. Human-factors engineering methods and expert feedback from surgeons, nurses, and anesthetists were used to evaluate the impact of intraoperative imaging on the surgical environment and clinical workflow. Image quality of CBCT and the perceived clinical utility were evaluated. RESULTS: The CBCT C-arm was successfully incorporated in 12 head and neck cases and streamlined into the surgical environment. Reviewed 3D-CBCT images were qualitatively sufficient for intraoperative-guidance for bony detail. Additional artifact management is required to improve soft-tissue visualization. CONCLUSIONS: Intraoperative CBCT provides high-quality images for visualization of bony detail and is feasible during major head and neck surgery with acceptable workflow interruptions. Operations with significant bone ablation and/or reconstruction involving complex 3D anatomical structures are likely to benefit from the updated imaging.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".