Development of the Antinuclear and Anticytoplasmic Antibody Consensus Panel by the Association of Medical Laboratory Immunologists
Bibliographic record
Abstract
The Association of Medical Laboratory Immunologists (AMLI) have developed a panel of antinuclear and anticytoplasmic antibody consensus sera that can be useful for enzyme immunoassay (EIA), Ouchterlony, and immunofluorescence assay methods. It was developed to assist in the evaluation of newly available EIA methods for the detection of autoantibodies. The panel of sera was evaluated in several clinical laboratories and a large number of laboratories owned by manufacturers of clinical autoantibody testing kits. The majority of sera performed well for the EIAs in both the clinical laboratories and the manufacturers' laboratories, but some samples had discrepant results. A major source of discrepancy is the current inability of the EIA results to be directly compared in a quantitative way as no standardization exists. The evaluation demonstrated lower sensitivity of detection by the Ouchterlony method. The limited evaluation of the sera with immunoblotting and Western blotting did not show good agreement with other methods. Further work must be done to standardize blotting methods prior to their use in routine clinical testing. The sera are now available to vendors and clinical laboratories for use in the detection of SS-A, SS-B, Sm, U1-RNP, Scl-70, Jo-1, double-stranded DNA, and centromere antibodies. The availability of the consensus sera will help evaluate and improve the EIA methods currently being used.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".