Incidence of transfusion reactions: a multicenter study utilizing systematic active surveillance and expert adjudication
Bibliographic record
Abstract
BACKGROUND: Prevalence estimates of the serious hazards of transfusion vary widely. We hypothesized that the current reporting infrastructure in the United States fails to capture many transfusion reactions and undertook a multicenter study using active surveillance, data review, and adjudication to test this hypothesis. STUDY DESIGN AND METHODS: A retrospective record review was completed for a random sample of 17% of all inpatient transfusion episodes over 6 months at four academic tertiary care hospitals, with an episode defined as all blood products released to a patient in 6 hours. Data were recorded by trained clinical research nurses, and serious reactions were adjudicated by a panel of transfusion medicine experts. RESULTS: Of 4857 transfusion episodes investigated, 1.1% were associated with a serious reaction. Transfusion-associated circulatory overload was the most frequent serious reaction noted, being identified in 1% of transfusion episodes. Despite clinical notes describing a potential transfusion association in 59% of these cases, only 5.1% were reported to the transfusion service. Suspected transfusion-related acute lung injury/possible transfusion-related acute lung injury, anaphylactic, and hypotensive reactions were noted in 0.08, 0.02, and 0.02% of transfusion episodes, respectively. Minor reactions, including febrile nonhemolytic and allergic, were noted in 0.62 and 0.29% of transfusion episodes, respectively, with 30 and 50% reported to the transfusion service. CONCLUSION: Underreporting of cardiopulmonary transfusion reactions is striking among academic, tertiary care hospitals. Complete and accurate reporting is essential to identify, define, establish pathogenesis, and mitigate/treat transfusion reactions. A better understanding of the failure to report may improve the accuracy of passive reporting systems.
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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 | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".