Elevated polyreactive immunoglobulin G in immune mediated liver injuries with the need for immunosuppressive therapy
Bibliographic record
Abstract
Abstract Background and aim The distinction of drug-induced liver injury (DILI), drug-induced autoimmune-like hepatitis (DI-ALH) and autoimmune hepatitis (AIH) can be challenging due to overlapping clinical characteristics. Recently, polyreactive immunoglobulin G (pIgG) was identified as a novel biomarker with a higher accuracy for the diagnose of AIH than conventional autoantibodies. This retrospective multicenter study aimed to evaluate the diagnostic accuracy of pIgG to distinguish between AIH, DI-ALH and DILI and thus identify patients in need of immunosuppression. Methods Samples from 116 patients (AIH=81, DI-ALH=12, DILI=23) were recruited and compared to a control group (non-AIH-non-DILI-LD= 596) from existing biorepositories. Results No patient in the DILI-group but 98% in the AIH-and 92% in the DI-ALH-group received immunosuppressive treatment. pIgG levels were significantly higher in the AIH-group (1.9 normalized arbitrary units (nAU) compared to DILI (1.1 nAU, p<0.001) and non-AIH-non-DILI-LD (1.0 nAU, p<0.001). Median pIgG concentrations of the DI-ALH-group (1.7 nAU) were between AIH (p=.634) and DILI (p=.052). Patients that needed immunosuppressive therapy for remission induction had significantly higher pIgG concentrations compared to those with spontaneous recovery of liver injury (1.8 nAU vs. 1.1 nAU, p<.001). The overall accuracy of pIgG >1.27nAU to distinguish AIH from DILI (74%) and liver injuries with and without the need for immunosuppression (74%) was similar to that of ANA (71/74%) and SMA (74/70%) at cut-offs of ≥1/40. Conclusion Polyreactive IgG can be used to predict AIH in comparison to DILI and indicate the need for immunosuppressive therapy in the work-up of immune mediated or drug-induced liver injuries.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".