Prediction of post-hepatectomy liver failure by preoperative gadoxetate disodium-enhanced magnetic resonance imaging
Bibliographic record
Abstract
BACKGROUND: Approximately 15% of patients experience post-hepatectomy liver failure after major hepatectomy. Poor hepatocyte uptake of gadoxetate disodium, a magnetic resonance imaging contrast agent, may be a predictor of post-hepatectomy liver failure. METHODS: A retrospective cohort study of patients undergoing major hepatectomy (≥3 segments) with a preoperative gadoxetate disodium-enhanced magnetic resonance imaging was conducted. The liver signal intensity (standardized to the spleen) and the functional liver remnant was calculated to determine if this can predict post-hepatectomy liver failure after major hepatectomy. RESULTS: In 134 patients, low signal intensity of the remnant liver standardized by signal intensity of the spleen in post-contrast images was associated with post-hepatectomy liver failure in multiple logistic regression analysis (Odds Ratio 0.112; 95% CI 0.023-0.551). In a subgroup of 33 patients with lower quartile of functional liver remnant, area under the curve analysis demonstrated a diagnostic accuracy of functional liver remnant to predict post-hepatectomy liver failure of 0.857 with a cut-off value for functional liver remnant of 1.4985 with 80.0% sensitivity and 89.3% specificity. CONCLUSION: Functional liver remnant determined by gadoxetate disodium-enhanced magnetic resonance imaging is a predictor of post-hepatectomy liver failure which may help identify patients for resection, reducing morbidity and mortality.
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 | 0.000 | 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) | 0.001 | 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".