Best practices in the differential diagnosis and reporting of acute transfusion reactions
Bibliographic record
Abstract
Christopher M Hillis,1–3,* Andrew W Shih,1,3,* Nancy M Heddle1,3,4 1Department of Medicine, 2Department of Oncology, 3McMaster Transfusion Research Program, McMaster University, Hamilton, 4Centre for Innovation, Canadian Blood Services, Ottawa, ON, Canada  *These authors contributed equally to this work Abstract: An acute transfusion reaction (ATR) is any reaction to blood, blood components, or plasma derivatives that occurs within 24 hours of a transfusion. The frequencies of ATRs and the associated symptoms, reported by the sentinel sites of the Ontario Transfusion Transmitted Injuries Surveillance System from 2008 to 2012, illustrate an overlap in presenting symptoms. Despite this complexity, the differential diagnosis of an ATR can be determined by considering predominant signs or symptoms, such as fever, dyspnea, rash, and/or hypotension, as these signs and symptoms guide further investigations and management. Reporting of ATRs locally and to hemovigilance systems enhances the safety of the blood supply. Challenges to the development of an international transfusion reaction reporting system are discussed, including the issue of jurisdiction and issues of standardization for definitions, investigations, and reporting requirements. This review discusses a symptom-guided approach to the differential diagnosis of ATRs, the evolution of hemovigilance systems, an overview of the current Canadian system, and proposes a best practice model for hemovigilance based on a World Health Organization patient safety framework. Keywords: blood transfusion, blood components, hemovigilance
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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 teacher head, 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".