Non‐invasive diagnostic imaging tests largely underdiagnose cardiac cirrhosis in patients undergoing advanced therapy evaluation: How can we identify the high‐risk patient?
Bibliographic record
Abstract
BACKGROUND: Patients with liver cirrhosis are generally considered ineligible for isolated cardiac transplantation or left ventricular assist device (LVAD) implantation. The aim of this retrospective study is to explore the diagnostic value of abdominal ultrasound, computed tomography scan (CT scan) and liver-spleen scintigraphy to detect the presence of cirrhosis in patients with advanced heart failure. METHODS: Among 567 consecutive patients who underwent pre-transplantation or LVAD evaluation, 54 had a liver biopsy to rule out cardiac cirrhosis; we compared the biopsy results with the imaging investigations. RESULTS: In about 26% (n = 14) of patients undergoing liver biopsy, histopathological evaluation identified cirrhosis. The respective sensitivity of abdominal ultrasound, CT scan and liver-spleen scintigraphy to detect cirrhosis was 57% [29-82], 50% [16-84], and 25% [3-65]. The specificity was 80% [64-91], 89% [72-98], and 44% [20-70], respectively. CONCLUSION: Ultrasonography has the best-combined sensitivity and specificity for the diagnosis of cirrhosis. However, more than a third of patients with cirrhosis will go undiagnosed by conventional imaging. As liver biopsy is associated with a low rate of complication, it should be considered in patients with a high-risk of cirrhosis or with evidence of portal hypertension to assess their eligibility for heart transplantation or LVAD implantation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".