The impact of an antibody investigation algorithm emphasizing specificity on reducing potential false‐positive warm autoantibody detection at a Canadian tertiary care centre
Bibliographic record
Abstract
Abstract Background and Objectives To reduce potential false‐positive warm autoantibody (WAA) by solid‐phase red cell adherence assay (SPRCA), our centre implemented a new antibody investigation algorithm (AIA) by classifying cases with panreactive SPRCA but negative saline‐indirect antiglobulin test as ‘antibody of undetermined significance’ (AUS) after excluding clinically significant antibodies. We assessed the effects of the new AIA and subsequent alloantibody formation in patients with AUS. Materials and Methods Samples from patients with positive SPRCA screens between 1 September 2017 and 31 August 2021 were selected for the study. Frequencies of antibodies classified by the old and new AIAs were compared using Fisher's exact test. Patient demographics, transfusion history and antibody formation in cases of AUS were collected. Results A significant reduction in potential WAA frequencies from 127/1167 (11%) to 53/854 (6%) was observed (p < 0.001) when compared between the old and new AIAs among 2021 positive SPRCA antibody screens. While no patients with AUS later transitioned to potential WAA using the new AIA, four patients developed alloantibodies, including anti‐E, anti‐C, both anti‐C and anti‐E, and anti‐Wra. Conclusion A significant reduction in the frequencies of potential false‐positive WAA detection at our centre was observed after implementing the new AIA, leading to less resource and phenotypically matched red blood cell (RBC) use. Some patients still developed subsequent RBC alloimmunization, so clinically relevant alloantibodies should be carefully excluded before determining AUS, taking forming or evanescent antibodies into consideration.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".