Surgical Planning of Hepatic Metastasectomy Using Radiologist-Performed Intraoperative Ultrasound
Bibliographic record
Abstract
Background: Intraoperative ultrasound (IOUS) of the liver is a useful adjunct for surgical planning during hepatic metastasectomy. This study aims to (1) report the frequency of change in operative plan as a result of IOUS findings and (2) determine whether IOUS is still beneficial after implementing a standardized, comprehensive process of preoperative hepatic imaging. Methods: First, a retrospective review of all patients undergoing hepatic metastasectomy at a single institution was conducted to identify how frequently IOUS findings altered the surgical plan. Second, a prospective study was conducted where patients underwent both preoperative CT and MRI within 30 days before surgery to determine if IOUS may still have benefit despite the implementation of a standardized preoperative imaging protocol. Results: In the retrospective review, 39 liver resections were completed; 100% and 36% of patients underwent preoperative CT and MRI, respectively. The mean time between preoperative imaging and surgery was 46 days (7-126). Operative plans were changed in 10/39 (26%) cases based on IOUS. After the standardization of preoperative imaging, 27 liver resections were performed. All patients underwent preoperative CT and MRI; the mean time between preoperative imaging and surgery was 20 days (1-98) (p=0.001). The operative plan was amended in 5/27 (19%) cases based on IOUS (χ 2=1.405, p=0.24). Conclusion: Even after standardizing the quality and timing of preoperative imaging, the operative plan was changed in nearly 1/5 patients due to IOUS. These findings demonstrate the utility of IOUS in surgical planning for hepatic metastasectomy and provide the basis for a quality improvement strategy regarding standardized preoperative 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 | 0.002 | 0.008 |
| 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 | 0.000 |
| 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 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".