The Utility of Monitoring Gamma-glutamyl Transpeptidase (GGT) Levels Post-liver Transplantation: A Longitudinal Retrospective Analysis
Bibliographic record
Abstract
Introduction: Recently, gamma-glutamyl transpeptidase (GGT) has garnered increased attention as a diagnostic tool in the early identification of liver disease. However, its value in liver transplantation (LT) is largely unknown, as the underlying disease processes leading to abnormal GGT values as well as the expected temporal trends in GGT during the post-LT period remain unclear. Methods: Between January 2010 and August 2013 consecutive patients who underwent LT at a single LT center were assessed longitudinally up to 1-year post-LT. A “GGT event” was defined as two abnormal GGT values (exceeding gender-specific limits of normal: females 55 U/L; males 80 U/L) ≥ 1-week apart. Results: 147 LT recipients were included. Median GGT levels on day-1 post-LT were 73 U/L, peaked at 435 U/L during the first month post-LT and returned to within normal parameters by 1-year. In total, there were 282 GGT events with biliary complications (22%), acute rejection (16%) and hepatitis C virus recurrence (10%) being the most common causes. In 39% of events, no cause was identified. When attempting to identify a disease-associated event, if GGT was the initial liver biochemistry test to double in value, it had 42% sensitivity and 40% specificity. Comparatively, if GGT was the initial liver biochemistry test to become abnormal, it had 3% sensitivity and 93% specificity. Conclusion: In conclusion, while GGT almost universally becomes abnormal during the post-LT period, a specific pathologic cause is commonly not identified. Interpreting the characteristics of GGT elevation has limited utility for identifying the underlying reason for its elevation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".