How Do Different Indices of Hepatic Enhancement With Gadoxetic Acid Compare in Predicting Liver Failure and Other Major Complications After Hepatectomy?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to assess the accuracy of gadoxetic acid hepatic enhancement indices in predicting posthepatectomy liver failure (PHLF) and other major complications (OMCs). METHODS: Sixty-five patients underwent prehepatectomy gadoxetic acid-enhanced magnetic resonance imaging. Enhancement indices were calculated by obtaining regions of interest on magnetic resonance images and segmented volumes of the liver and spleen. Multivariate regression analysis was performed to predict PHLF and OMC as a function of the indices, and areas under the receiver operator characteristic (AUROC) curves were calculated. RESULTS: Areas under the receiver operator characteristic values varied from 0.412 to 0.681 and 0.462 to 0.738 in predicting PHLF and OMC, respectively. The most accurate indices in predicting PHLF were the region of interest-based, fat-normalized relative liver enhancement and liver enhancement index (AUROC, 0.681). The most accurate index in predicting OMC was the volumetric least-squares regression slope of a pharmacokinetic model (Khep_V, AUROC, 0.738). CONCLUSIONS: Indices of gadoxetic acid liver enhancement demonstrate variable performance in predicting PHLF and OMC.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".