Gamma‐Glutamyl Transferase (γ‐GT) – an old dog with new tricks?
Bibliographic record
Abstract
Gamma-Glutamyl Transferase (γGT) is a key transferase involved in the transpeptidation of functional gamma-glutamyl groups to various receptor moieties. It performs important roles in antioxidant defence mechanisms, particularly glutathione recycling, xenobiotic metabolism, but analogously may also have a pro-oxidant role. γGT is very sensitive for the diagnosis of liver injury, although it has poor specificity for particular aetiologies. It has been used to reflect temporal changes as a form of monitoring depending on aetiology. Given its cellular role in antioxidant function, it has been investigated as a surrogate biomarker of oxidative stress. It has also been found to be a predictor of mortality across a spectra of non-hepatic disease pathologies, from metabolic and cardiovascular risk to chronic kidney disease and neoplasia. Similarly, it also remains of interest to the insurance industry given an apparent ability to predict mortality, in addition to a historical interest from law enforcement as a marker of chronic alcohol ingestion. Here, we review some of the unique characteristics of this important enzyme, previously considered as a mere specific marker of liver dysfunction, but now with clear extra-hepatic implications and novel applications and utility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".