Spectrum of autoimmune liver diseases in western India
Bibliographic record
Abstract
BACKGROUND AND AIM: The prevalence and spectrum of autoimmune liver diseases (AILDs) in India are rarely reported in comparison to the West. METHOD: During a study period of 7 years, all patients with chronic liver diseases (CLDs) were evaluated for the presence of AILDs on the basis of clinical, biochemical, imaging, serological, and histological characteristics. RESULTS: Of a total of 1760 CLD patients (38.1% females), 102 patients (5.7%) had an AILD. A total of 75 (11.2%) female patients had an AILD. Among males, 27 (2.4%) had an AILD. The prevalence of AILDs in women increased from 11.2% to 45.7% and in men from 2.4% to 10.3%, after excluding alcohol, hepatitis B virus, and hepatitis C virus as a cause of CLD. Of the AILDs, autoimmune hepatitis (AIH) was present in 79 patients (77.4%), followed in descending order by primary biliary cirrhosis (PBC) in 10 patients (9.8%), PBC/AIH true overlap syndrome in six patients (5.8%), primary sclerosing cholangitis (PSC) in five patients (4.9%), and PBC/AIH switchover syndrome in two patients (1.9%). None had PSC/AIH or PBC/PSC overlap syndrome. Associated known autoimmune diseases were found in 40 (39.2%) patients. CONCLUSIONS: AILDs are not uncommon in India. They should be suspected in all cases of CLDs, especially in middle-aged women who do not have problems with alcoholism and who are without viral etiology, as well as in all patients with known autoimmune diseases.
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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 | 0.000 |
| 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) | 0.001 | 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".