Non-alcoholic steatohepatitis in liver transplant recipients diagnosed by serum cytokeratin 18 and transient elastography: A prospective study
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) seem common after liver transplantation. AIM: To investigate incidence and predictors of NAFLD and NASH by employing noninvasive testing in liver transplant recipients, namely controlled attenuation parameter (CAP) and the serum biomarker cytokeratin 18 (CK-18). We also evaluated the diagnostic accuracy of CK-18 and CAP compared to liver histology. METHODS: We prospectively recruited consecutive adult patients who received liver transplant at the McGill University Health Centre between 2015-2018. Serial measurements of CK-18 and CAP were recorded. NAFLD and NASH were diagnosed by CAP ≥ 270 dB/m, and a combination of CAP ≥ 270 dB/m with CK-18 > 130.5 U/L, respectively. Incidences and predictors of NAFLD and NASH were investigated using survival analysis and Cox proportional hazards. RESULTS: < 0.01). Compared to liver histology, CAP had a 76% accuracy to diagnose NAFLD, while the accuracy of CAP plus CK-18 to diagnose NASH was 82%. CONCLUSION: NAFLD and NASH diagnosed non-invasively are frequent in liver transplant recipients within the first 18 mo. Close follow-up and nutritional counselling should be planned in overweight patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".