Abbreviated Multiparametric MR Solution (the “Liver Triple Screen”), the Future of Non-Invasive MR Quantification of Liver Fat, Iron, and Fibrosis
Bibliographic record
Abstract
Background/Objectives: To review the findings of a multiparametric MRI (the “liver triple screen”) solution for the non-invasive assessment of liver fat, iron, and fibrosis in patients with chronic liver disease (CLD). Methods: A retrospective evaluation of all consecutive triple screen MRI cases was performed at our institution over the last 32 months. Relevant clinical, laboratory, and radiologic data were analyzed using descriptive statistics. Results: There were 268 patients, including 162 (60.4%) males and 106 (39.6%) females. The mean age was 54 ± 15.2 years (range 16 to 71 years). The most common cause of CLD was metabolic dysfunction-associated steatotic liver disease (MASLD) at 45.5%. The most common referring physician group was Gastroenterology at 62.7%. In 23.9% of cases, the reason for ordering the MRI was a pre-existing failed or unreliable US elastography. There were 17 cases (6.3%) of MRI technical failure. Our analysis revealed liver fibrosis in 66% of patients, steatosis in 68.3%, and iron overload in 22.1%. Combined fibrosis and steatosis were seen in 28.7%, steatosis and iron overload in 16.8%, fibrosis and iron overload in 6%, and combined fibrosis, steatosis, and iron overload in 4.1%. A positive MEFIB index, a predictor of liver-related outcomes, was found in 57 (27.5%) of 207 patients. Incidental findings were found in 14.9% of all MRIs. Conclusions: The liver triple screen MRI is an effective tool for evaluating liver fat, iron, and fibrosis in patients with CLD. It provides essential clinical information and can help identify MASLD patients at risk for liver-related outcomes.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".