A revised classification scheme for acute transfusion reactions
Bibliographic record
Abstract
BACKGROUND: Although the standard classification system for acute transfusion reactions adequately describes the general features associated with the various types of reactions, it was not designed to provide strict criteria for diagnosis and classification. Consequently, its use to classify individual reactions can result in significant inter- and intraobserver variability, which can complicate patient management and clinical research. STUDY DESIGN AND METHODS: A total of 595 transfusion reactions that occurred at a single institution between January 1, 1996, and December 31, 2003, were reviewed and were initially classified according to the established conventions of the AABB. Each reaction was then reclassified with a revised system that refines and clarifies reaction categories, adds severity grades in the format of the National Cancer Institute's Common Terminology Criteria for Adverse Events (CTCAE), and includes terminology to indicate the attribution or likelihood that the adverse event is related to the transfusion. RESULTS: Comparison of the two approaches as applied to these 595 transfusion reactions showed clear advantages for the revised system. Of 128 reactions classified by AABB criteria as inconclusive, a mixture of reaction types, or otherwise qualified, all but 5 were accommodated by discrete categories within our revised scheme. In each case with a classifiable reaction, the severity of the reaction could be readily graded. CONCLUSION: The advantages of this revised classification scheme for acute transfusion reactions warrant prospective evaluation and ultimately consideration of its incorporation into clinical practice.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".