Lack of complete biochemical response in autoimmune hepatitis leads to adverse outcome: First report of the IAIHG retrospective registry
Bibliographic record
Abstract
BACKGROUND AND AIMS: The International Autoimmune Hepatitis Group retrospective registry (IAIHG-RR) is a web-based platform with subjects enrolled with a clinical diagnosis of autoimmune hepatitis (AIH). As prognostic factor studies with enough power are scarce, this study aimed to ascertain data quality and identify prognostic factors in the IAIHG-RR cohort. METHODS: This retrospective, observational, multicenter study included all patients with a clinical diagnosis of AIH from the IAIHG-RR. The quality assessment consisted of external validation of completeness and consistency for 29 predefined variables. Cox regression was used to identify risk factors for liver-related death and liver transplantation (LT). RESULTS: This analysis included 2559 patients across 7 countries. In 1700 patients, follow-up was available, with a completeness of individual data of 90% (range: 30-100). During a median follow-up period of 10 (range: 0-49) years, there were 229 deaths, of which 116 were liver-related, and 143 patients underwent LT. Non-White ethnicity (HR 4.1 95% CI: 2.3-7.1), cirrhosis (HR 3.5 95% CI: 2.3-5.5), variant syndrome with primary sclerosing cholangitis (PSC) (HR 3.1 95% CI: 1.6-6.2), and lack of complete biochemical response within 6 months (HR 5.7 95% CI: 3.4-9.6) were independent prognostic factors. CONCLUSIONS: The IAIHG-RR represents the world's largest AIH cohort with moderate-to-good data quality and a relevant number of liver-related events. The registry is a suitable platform for patient selection in future studies. Lack of complete biochemical response to treatment, non-White ethnicity, cirrhosis, and PSC-AIH were associated with liver-related death and LT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".