Comparing transfusion reaction risks for various plasma products – an analysis of 7 years of ISTARE haemovigilance data
Bibliographic record
Abstract
Summary Plasma transfusions may result in transfusion reactions. We used the International Surveillance of Transfusion‐Associated Reactions and Events (ISTARE) database, containing yearly reported national annual aggregate data on transfusion reactions from participating countries, to investigate risks of plasma transfusion reactions and compare transfusion reaction risks for different plasma types. We calculated risks for plasma transfusion reactions and compared transfusion reaction risks between plasma types using random effects regression on repeated measures. The ISTARE database contains data from 23 countries, reporting units issued and/or transfused and transfusion reactions observed for some portion of 7 years (2006–2012). Interquartile ranges (IQRs) of plasma transfusion reaction risks were: allergic reactions (5·6–72·2 reactions/105 units transfused); febrile non‐haemolytic transfusion reactions (0–9·1); transfusion‐associated circulatory overload (0–1·9); transfusion related acute lung injury (TRALI) (0–1·2); and hypotensive reactions (0–0·6). Apheresis plasma was associated with more allergic reactions [odds ratio (OR) = 1·29 (95% confidence interval: 1·19–1·40)] and hypotensive reactions [OR = 2·17 (1·38–3·41)] than whole blood‐derived plasma. Pathogen‐inactivated plasma was associated with fewer transfusion reactions than untreated plasma.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".