Serologic assessments in acute transfusion reactions: practices and yields
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Serologic testing after transfusion reactions (TRs) aims to find accountable immune haemolytic incompatibility. Our hospital policies recommend serologic testing in all TR, except for low-risk fevers (subclinical temperature <39°C) or uncomplicated allergic reactions. Assessing compliance with these guidelines and serologic testing yields may provide insights on quality of practice and value. MATERIALS & METHODS: Interrogation of two haemovigilance databases identified all possible-to-definite TR over a 4-year period (2013-2016) at four academic hospitals. We reviewed the performance and outcome of serologic testing by site, year, reaction type, implicated product and service. RESULTS: Serologic testing occurred in 769 (55%) of 1408 referrals, with 1153 (82%) compliant with guidelines. Similar proportions deviated to overtesting (85/550 [15%]) and undertesting (174/858 [20%]), with undertesting seen most often in atypical TR. Overall, 30 (4.4%) of 769 cases had a new finding, but only 2 (0.3%) reflected host-derived antibodies. Overall, the number needed to test to discover an unexpected allospecificity was 385, or 253 if limited to high-risk fevers. Reaction- and product-specific yields ranged from 0% to 48%. The yield in complicated allergic reactions was low at 2%, constituting only predictable passive isohaemagglutinin(s) in retrospect. Investigated IVIG TR accounted for most of this cohort's signal by passive isohaemagglutinins in 48%. CONCLUSION: The performance of post-TR serologic testing revealed practice gaps and expected context-specific yields. Tailored serologic testing (i.e. indirect antiglobulin tests for alloantibodies in post-RBC/high-risk febrile reactions, ± isoagglutinin-focused tests after IVIG or ABO-minor-mismatched platelets) may improve value and liberate resources for other unmet needs in TR investigation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".