Laparoscopic Radiofrequency Thermal Ablation of Hepatocellular Carcinoma in Liver Cirrhosis Patients
Bibliographic record
Abstract
BACKGROUND: Laparoscopic radiofrequency ablation (LRFA) for hepatocellular carcinoma (HCC) under guidance of intra-operative laparoscopic ultrasound (IOLUS) aiming of obtaining additional information for liver situation, better tumor staging and effective treatment of hepatic focal lesion (HFL) in patients with a difficult percutaneous approach. METHODS: Between September 2010 and July 2012, 301 patients with HCC in liver cirrhosis were referred from HCC clinic at National Hepatology and Tropical Medicine Research Institute (NHTMRI). Twenty nine patients were submitted to LRFA with IOLUS guidance. Operation time, hospital stay, post procedure complication were recorded. Spiral CT scan one month postoperative was mandatory during follow up. RESULTS: LRFA was completed in all patients. The IOLUS examination identified new HFL in three patients. A total of 32 lesions were treated. The mean operative time was 120 minutes; eight procedures were associated in six patients: cholecystectomy (6) and adhesiolysis (2). A complete tumor ablation was observed in all patients which were documented via spiral computed tomography (CT scan) one month after treatment. CONCLUSION: LRFA of HCC proved to be a safe and effective technique. IOLUS is superior on spiral CT scan in detection a small HCC.
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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 | 0.001 |
| 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) | 0.002 | 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".