Objective evaluation of liver consistency to estimate hepatic fibrosis and functional reserve for hepatectomy1
Bibliographic record
Abstract
BACKGROUND: The empiric evaluation of liver consistency is currently used to plan the surgical strategy. The aim of this study was to verify the feasibility of the objective measurement of liver consistency and to check its correlation with liver fibrosis and liver functional reserve. STUDY DESIGN: Fifty-two consecutive patients who underwent hepatic resections in our department were enrolled. The indications for liver resection were hepatocellular carcinoma in 36 patients, metastatic liver tumors in 12 patients, and other conditions in 4 patients. Liver consistency was measured with a new tactile sensor. A fibrosis index was calculated as an expression of the percentage of fibrotic tissue. Liver consistency was compared with the degree of liver fibrosis observed in histologic specimens (fibrosis index) and with liver function parameters. RESULTS: Liver stiffness showed a significant positive correlation with fibrosis index (r = 0.887, p < 0.0001). Liver stiffness also showed significant positive correlation with the indocyanine green test (r = 0.631, p < 0.0001) by a univariate analysis. The indocyanine green test and platelet count were independently and significantly associated with liver stiffness by a multiple regression analysis. In five patients, the liver stiffness values measured intraoperatively differed markedly from those expected from the indocyanine green test values. In these patients, the operative procedures were finally selected based on the liver stiffness measured with the tactile sensor and good clinical outcomes were obtained. CONCLUSIONS: These results show for the first time that liver stiffness can be clinically assessed quantitatively by means of the tactile sensor. The tactile sensor adequately estimates liver stiffness and this estimation is well correlated with liver fibrosis and functional reserve. Liver consistency determined objectively in this manner may be useful for optimizing surgical decision making.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".