Improved diagnoses of autoimmune hepatitis using an anti‐actin ELISA
Bibliographic record
Abstract
The presence of antismooth muscle antibodies is one of the diagnostic criteria of autoimmune hepatitis. We evaluated a new anti-F-actin ELISA test and compared it with indirect immunofluorescence assay (IIFA) for antismooth muscle antibodies (ASMA). Two hundred and nine serum samples (35 autoimmune hepatitis, 174 other hepatopathies and control sera) were tested by IIFA on mouse stomach kidney sections for ASMA and by the Quanta Lite Actin ELISA for anti-F-actin antibodies. ASMA were detected in 26 of 35 sera from autoimmune hepatitis (74%) as compared with 25 (71%) with anti-actin antibodies, as well as in 25 of 49 (51%) samples from viral hepatitis as compared with 7 (14%) with anti-actin antibodies. With regards to autoimmune hepatitis, though sensitivity (74.3 vs 71.4%) and negative predictive value (93.5 vs 93.9%) of ASMA and anti-actin ELISA were comparable, anti-actin ELISA was significantly better than ASMA IIFA in terms of specificity (89.7 vs 74.7%), and positive predictive value (58.1 vs 37.1%). Although frequently positive in HCV samples, a comparable sensitivity but better specificity makes the anti-actin ELISA a useful tool in combination with ASMA IIFA for the screening and diagnosis of autoimmune hepatitis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".