Severe vitamin D deficiency is a prognostic biomarker in autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND: Vitamin D deficiency has been implicated in the outcome of chronic liver disease. AIM: To determine the frequency of severe vitamin D deficiency in autoimmune hepatitis (AIH), assess its association with treatment non-response, and evaluate the relationship between vitamin D status and liver-related mortality and need for transplantation. METHODS: Two hundred and nine patients were evaluated by liver tissue examination at presentation. Serum vitamin D levels were determined, and serum levels <25 nmol/L (10 ng/mL) were considered severely deficient. Treatment non-response was defined as non-normalised aspartate aminotransferase/alanine aminotransferase and immunoglobulin G levels during conventional immunosuppressive therapy. Univariate and multivariate analyses were performed using binary logistic regression and Cox proportional hazards model. RESULTS: The mean vitamin D level was 60 ± 38 nmol/L (range, 3-263 nmol/L), and 42 patients (20%) had severe vitamin D deficiency. Treatment non-response was more common in patients with severe vitamin D deficiency than in patients without (59% vs 41%, P = 0.04). Severe vitamin D deficiency was also independently associated with a higher risk of developing cirrhosis (HR 3.40; 95% CI 1.30-8.87, P = 0.01) and liver-related mortality or requirement for liver transplantation (LT; HR 5.26, 95% CI, 1.54-18.0, P = 0.008). Patients with persistent severe deficiency following vitamin D supplementation continued to have poor outcomes. CONCLUSIONS: Severe vitamin D deficiency is associated with treatment non-response, progression to cirrhosis, and liver-related death or need for LT. Severe vitamin D deficiency is a prognostic biomarker in AIH.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".