Cirrhosis and Lesion Characterization at MR Imaging
Bibliographic record
Abstract
Magnetic resonance (MR) imaging has emerged as an important imaging modality for the assessment of cirrhosis and its complications. Faster sequences now allow high-quality liver imaging with high intrinsic soft-tissue contrast. Automated contrast detection methods in combination with faster sequences allow reproducible capture of the arterial phase, which is essential for the detection and characterization of hepatocellular carcinoma. The lack of ionizing radiation permits routine use of gadolinium-enhanced three-dimensional (3D) fat-suppressed multiphasic imaging with high temporal and spatial resolution. In addition, MR imaging allows simultaneous evaluation of the background liver parenchyma and the liver lesions with the combined use of sequences that include T2-weighted sequences, T1-weighted sequences (including chemical shift imaging), echoplanar diffusion-weighted sequences, dynamic gadolinium-enhanced 3D multiphasic imaging, and liver-specific delayed phase sequences (if contrast agents with hepatobiliary excretion are used). The combination of findings from different sequences often helps pinpoint the nature of the liver abnormalities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".