Evaluation of immunoserological detection of anti-liver kidney microsomal, anti-soluble liver antigen and anti-mitochondrial antibodies
Bibliographic record
Abstract
Autoantibodies are the diagnostic hallmark of autoimmune liver diseases. Indirect immunofluorescence (IFT) is the reference method for the detection of anti-mitochondrial antibodies (AMA) and anti-liver kidney microsomal type-1 (anti-LKM1) antibodies, and inhibition ELISA (iELISA) for anti-soluble liver antigen (anti-SLA) antibodies. Given the complexity of these techniques, commercial ELISAs have emerged as a practical alternative, but without head-to-head validations. This study evaluated the agreement between three commercial ELISAs and the reference techniques and the impact of polyreactive immunoglobulin G (pIgG), a recently described phenomenon in autoimmune hepatitis, on commercial ELISAs. Inter-rater reliability was assessed using Cohen-Kappa coefficient (κ). Forty-eight, 46, and 66 samples were analyzed for AMA, anti-LKM1, and anti-SLA, respectively. For AMA, one commercial assay showed high agreement (κ = 0.91 (0.78-1.00)) with the reference method, while the other two showed weak or moderate agreement. For anti-LKM1, only one commercial assay showed high agreement (κ = 0.86 (0.71-1.0)). For anti-SLA antibodies only moderate agreement was achieved (κ up to 0.71 (0.52-0.89)). There was a trend towards higher pIgG levels in false-positives in the commercial ELISAs. Patients with high suspicion of autoimmune liver diseases should be referred to reference laboratories with the capacity of performing gold standard methods if the initial ELISA-based screening was performed.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".