Intermittent Hepatic Inflow Occlusion During Partial Hepatectomy for Hepatocellular Carcinoma Does Not Shorten Overall Survival or Increase the Likelihood of Tumor Recurrence
Bibliographic record
Abstract
AIM: To investigate whether the long-term outcomes of hepatocellular carcinoma (HCC) was adversely impacted by intermittent hepatic inflow occlusion (HIO) during hepatic resection. METHODS: 1549 HCC patients who underwent hepatic resection between 1998 and 2008 were identified from a prospectively maintained database. Intermittent HIO was performed in 931 patients (HIO group); of which 712 patients had a Pringle maneuver as the mechanism for occlusion (PM group), and 219 patients had selective hemi-hepatic occlusion (SO group). There were 618 patients that underwent partial hepatectomy without occlusion (occlusion-free, OF group). RESULTS: The 1-, 3-, and 5- year overall survival (OS) rates were 79%, 59%, and 42% in the HIO group, and 83%, 53%, and 35% in the OF group, respectively. The corresponding recurrence free survival (RFS) rates were 68%, 39%, and 22% in the HIO group, and 74%, 41%, and 18% in the OF group, respectively. There was no significant difference between the 2 groups in OS or RFS (P=0.325 and P=0.416). Subgroup analysis showed patients with blood loss over 3000 mL and those requiring transfusion suffered significantly shorter OS and RFS. Blood loss over 3000 mL and blood transfusion were independent risk factors to OS and RFS. CONCLUSIONS: The application of intermittent HIO (PM and SO) during hepatic resection did not adversely impact either OS or RFS in patients with HCC. Intermittent HIO is still a valuable tool in hepatic resection, because high intraoperative blood loss resulting in transfusion is associated with a reduction in both OS and RFS.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".