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 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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".