Incidence of transfusion reactions: a multicenter study utilizing systematic active surveillance and expert adjudication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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