Investigation of C‐Arm Cone‐Beam CT‐Guided Surgery of the Frontal Recess
Bibliographic record
Abstract
OBJECTIVE/HYPOTHESIS: A cone-beam CT (CBCT) imaging system based on a mobile C-arm (Siemens PowerMobil) incorporating a high-performance flat-panel detector (Varian PaxScan) has been developed in our laboratory. We hypothesize that intraoperative C-arm CBCT provides image quality and guidance performance sufficient to assist surgical approach to the frontal recess. STUDY DESIGN: A preclinical prospective study was conducted using six cadaver heads to assess the performance characteristics and the potential clinical utility of this imaging system. METHODS: The mobile C-arm was employed for intraoperative CBCT guidance of the endoscopic approach to twelve frontal recesses. RESULTS: The imaging system is capable of sub-mm 3D spatial resolution with bone and soft-tissue visibility and a field of view sufficient for guidance of head and neck surgery. The system can generate intraoperative, volumetric CT images rapidly with an acceptably low radiation exposure to the patient and with image quality sufficient for most surgical tasks. Moreover, the system is portable and compatible with the surgical setup, providing excellent access to the patient. Finally, the accuracy of the system is not bound to a registration process. CONCLUSIONS: The ability to create updated images as surgery progresses introduces the concept of 'near-real-time' CT guidance for head and neck surgery. We found that the use of CBCT increased surgical confidence in accessing the frontal recess, resolved ambiguities with anatomical variations, and provided valuable teaching information to surgeons in training in both preoperative planning and correlation between tri-planar CT scans and intraoperative endoscopic findings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".