Warm‐reactive (immunoglobulin G) autoantibodies and laboratory testing best practices: review of the literature and survey of current practice
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Warm-reactive autoantibodies (WAAs) are the most common cause of autoimmune hemolytic anemia (AIHA) and can also be present without clinically significant hemolysis. WAAs complicate immunohematological testing, yet there is no commonly accepted approach to laboratory evaluation and red blood cell (RBC) selection. STUDY DESIGN AND METHODS: We searched PubMed/Cochrane Central for articles that described testing methodology and blood selection for patients with WAAs. We developed a 31-question survey regarding local practice for immunohematology testing and RBC selection in patients with WAAs (with or without AIHA). RESULTS: Eighty-six studies met the inclusion criteria and the aims of this review. Most of the literature was comprised of retrospective studies that often did not correlate laboratory results with clinical findings. Evidence-based protocols to guide testing and RBC selection for transfusion in patients with WAAs are lacking. Individuals representing 54 laboratories completed the survey. The responses indicated that numerous methodologies are used to identify underlying alloantibodies: 75% of respondents use autoadsorption; in patients who have a recent history of transfusion, 76% of respondents use alloadsorption; 58% of respondents perform direct antiglobulin testing (DAT) each time the indirect antiglobulin test is positive; and 48% perform eluate studies at the initial identification of WAAs. Responding laboratories may use phenotyping (98%) or genotyping (80%) at some point in the work-up. Seventy-five percent of respondents provide phenotype-matched or genotype-matched RBCs for transfusion. CONCLUSION: There is wide variability in immunohematology testing and RBC selection practices for patients who have WAAs (with or without AIHA). Future studies are needed to evaluate and compare the effectiveness of different testing algorithms and transfusion strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it