The image‐guided operating room—Utility and impact on surgeon's performance in the head and neck surgery
Bibliographic record
Abstract
BACKGROUND: The image-guided operating room (OR) is an emerging standard for dealing with complex cases in many surgical disciplines including neurosurgery, thoracic surgery, maxillofacial trauma, and orthopedic surgery. Its use in head and neck oncological surgery is not well established. The primary aim of this study was to assess the image quality of cone-beam CT (CBCT) under real clinical conditions. The secondary aim was to assess the effect on surgical performance and decision making. METHODS: Intraoperative 3D imaging was performed using a CBCT capable C-Arm mounted on a multi-axis robot (Siemens Zeego) in the image-guided OR. All patients had immediate preoperative imaging taken with further intraoperative imaging performed as required. Ten initial patients, comprising 28 intraoperative scans, were used for questionnaire-based image reviews conducted with experienced head and neck clinicians. Scans were assessed for aspects of both image quality and clinical utility, on separate 5-point Likert scales (1-5). RESULTS: ) from 1 to 3 with the use of IV contrast (region of interest CT# was 284 HU [SD, 47 HU]). Images were rated as 4 for freedom from artifact. Soft tissue definition was 2, with no significant improvement (P = .2) with the addition of IV iodinated contrast. Surgeons rated the greatest clinical utility (4) for the CBCT when assessing postreconstruction imaging of a complex case. CONCLUSIONS: The image quality of CBCT in the image-guided OR is good for bony detail and complex oncological reconstructions in the head and neck setting but probably has limited benefit for intraoperative soft tissue delineation. Future studies must also focus on clinical outcomes to help demonstrate the value of the image-guided OR.
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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 | 0.002 | 0.013 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".