Intraoperative ultrasonography and surgical strategy in hepatic resection: What difference does it make?
Bibliographic record
Abstract
BACKGROUND: With modern advancements in preoperative imaging for liver surgery, intraoperative ultrasonography (IOUS) may be perceived as superfluous. Our aim was to determine if IOUS provides new information that changes surgical strategy in hepatic resection. METHODS: We retrospectively analyzed 121 consecutive liver resections performed at a single institution. Preoperative computed tomography and/or magnetic resonance imaging determined the initial surgical strategy. The size, location and number of lesions were compared between IOUS and preoperative imaging. Reviewing the operative report helped determine if new IOUS findings led to changes in surgical strategy. Pathology reports were analyzed for margins. RESULTS: Of 121 procedures analyzed, IOUS was used in 88. It changed the surgical plan in 15 (17%) cases. Additional tumours were detected in 10 (11%) patients. A change in tumour size and location were detected in 2 (2%) and 3 (4%) patients, respectively. Surgical plans were altered in 7 (8%) cases for reasons not related to IOUS. There was no significant difference (p = 0.74) in average margin length between the IOUS and non-IOUS groups (1.09 ± 1.18 cm v. 1.18 ± 1.05 cm). CONCLUSION: Surgical strategy was altered owing to IOUS results in a substantial number of cases, and IOUS-guided resection planes resulted in R0 resections in nearly all procedures. The best operative plan in hepatic resection includes IOUS.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".