The current role of intraoperative ultrasound during the resection of colorectal liver metastases: A retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Liver resections with negative margins improve survival in patients with colorectal liver metastases (CRLM). Intraoperative ultrasound (IOUS) is a valuable tool that gives information about lesions that ultimately changes surgical strategy to ensure complete removal, which subsequently improves disease free survival (DFS). METHODS: A retrospective review of patients who underwent a resection for CRLM from 2009 to 2012 was completed to determine the impact of IOUS. RESULTS: A total of 103 patients had a hepatic resection for CRLM. All patients had preoperative imaging to assist with operative planning. IOUS was performed in 72 cases. Surgical strategy changed in 31 (43.1%) cases with IOUS, compared to three (9.7%) with no IOUS (P < 0.001). A new lesion was detected in 13 (18.1%) of the cases. A higher proportion of nonanatomic liver resections were performed in the IOUS group (N = 27, 37.5%) compared to the non-IOUS group (N = 6, 19.4%) (P = 0.07). CONCLUSION: Achievement of a negative resection margin was comparable between the two groups. However, there was a trend toward improved DFS in the IOUS group. Despite advances in preoperative imaging, IOUS demonstrates utility in providing novel information that allows removal of the entire tumor burden, using parenchymal-preserving techniques when feasible, leading to improved DFS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".