Evaluation of particle gel immunoassays for the detection of severe immunoglobulin A deficiency and anti‐human immunoglobulin A antibodies
Bibliographic record
Abstract
BACKGROUND: Immunoglobulin A (IgA)-deficient patients with anti-IgA (Ab) require transfusions using blood components with less than 0.05 mg IgA/dL as they are known to be safe for these patients. Identification of severely IgA-deficient (IgA SD) donors involved preliminary screening by the Ouchterlony double immunodiffusion assay followed by confirmatory testing at the required level of sensitivity for IgA and Ab at an external reference laboratory. Two in vitro particle gel immunoassays (ID-PaGIA IgA deficiency test and anti-IgA test) were also evaluated for their suitability in identifying IgA SD individuals and determining their Ab status. STUDY DESIGN AND METHODS: Samples from 198 donors and 36 patients, subjected to confirmatory testing for IgA SD and Ab over a 2-year period, were also evaluated using the ID-PaGIA kits. RESULTS: DiaMed test sensitivity and specificity for detection of IgA SD in donors was 98% whereas for Ab, test sensitivity was 91% at a specificity of 94%. In patients, sensitivity was 94% for IgA SD and 67% for Ab, both tests at a specificity of 100%. CONCLUSIONS: The ID-PaGIA IgA deficiency test was a sensitive and specific tool for identifying IgA SD donors or patients. Sensitivity of the Ab test was high for donors but reduced for patients and of high specificity in both groups. Further studies with patients are needed to confirm this latter observation. Implementation of these tests would make it possible to supply appropriate products from IgA SD donors to prevent anaphylactic transfusion reactions in patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".