Serum immunoglobulin A levels and alcohol-induced liver disease
Bibliographic record
Abstract
Background: Recent data suggest intestinal immunity including immunoglobulin A (IgA) may contribute to the pathogenesis of alcohol-induced liver disease (ALD). Methods: We documented serum IgA levels in ALD patients and determined whether those with elevated levels of IgA (E-IgA) had similar, more, or less advanced disease and different rates of progression than those with normal levels of IgA (N-IgA). Standard liver function tests (bilirubin, international normalized ratio [INR], and albumin), model for end-stage liver disease (MELD), and Fibrosis-4 (FIB-4) scores were used as indicators of disease severity. Results: From the study centre’s clinical database, we identified 175 adult patients with ALD, 107 (61%) with E-IgA and 68 (39%) with N-IgA. Gender distribution and mean age of the two cohorts were similar. E-IgA patients had biochemical evidence of more advanced liver disease (higher serum bilirubin and INR and lower albumin levels) than N-IgA patients ( ps < .05). E-IgA patients also had significantly higher median MELD and FIB-4 scores ( ps < .01). A higher percentage of E-IgA patients had FIB-4 values in keeping with advanced fibrosis or cirrhosis (55% versus 28%, p = .02). After mean follow-up periods of approximately 4 years, liver biochemistry and MELD and FIB-4 scores changed to similar extents in the two cohorts. Conclusions: Serum IgA levels were increased in approximately 70% of ALD patients. Although these patients had biochemical and non-invasive indicators of more advanced disease, elevations in serum IgA levels do not predict disease progression; therefore, IgA is unlikely to be of importance in the pathogenesis of ALD.
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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 | 0.000 | 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".