Establishment of an immunoglobulin A–deficient blood donor registry with a simple in‐house screening enzyme‐linked immunosorbent assay
Bibliographic record
Abstract
BACKGROUND: Transfusion of blood products to immunoglobulin A (IgA)-deficient patients who have developed IgA antibodies can result in serious adverse reactions. To prepare compatible blood components for these patients, blood centers usually maintain a list of IgA-deficient blood donors. An in-house enzyme-linked immunosorbent assay (ELISA) was used to identify new IgA-deficient blood donors. STUDY DESIGN AND METHODS: An in-house ELISA was used to screen blood samples. IgA-deficient samples, defined as an IgA level below 0.05 mg per dL, were sent to the American Red Cross for confirmatory testing. RESULTS: Seventy-three confirmed IgA-deficient blood donors were identified among 38,759 screened blood donor samples (frequency, 1:531). IgA antibodies were found in 39 of these 73 blood donors (53%), although only 9 donors had a history of adult IgA exposure (transfusion or pregnancy). CONCLUSIONS: With a simple in-house ELISA, 73 blood donors were identified as IgA-deficient. From this number, 34 donors, without detectable anti-IgA in their plasma, were added to our IgA-deficient blood donor panel to maximize the management of our inventory of IgA-deficient frozen blood components.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".