Hemovigilance data: An effective approach for evaluating bacterial protection systems for platelet transfusions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Septic transfusion reactions due to bacterial contamination in platelet concentrates (PCs) are continually reported to hemovigilance (HV) programs. Worldwide, blood centers use different systems to avoid transfusion-associated bacterial sepsis in PCs. Herein, national HV data were gathered to compare bacterial protection systems and to assess the risk of bacterial contamination. MATERIALS AND METHODS: HV data with definite transfusion-associated bacterial sepsis in PCs were obtained from Australia, Canada, the United Kingdom (U. K.), and Switzerland between 2006 and 2016. These data were reviewed to evaluate bacterial protection systems including early small-volume (ESV), early large-volume (ELV), and delayed large-volume (DLV) bacterial culture screening and pathogen inactivation (PI) treatment. RESULTS: < 0.05) in transfusion-associated bacterial sepsis for the period of 2011-2016 compared to the prior 4 years (2006-2010). Approximately 1.86 million DLV bacterial culture-screened PCs and 0.21 million PI-treated PCs were issued with no reported septic fatalities nor cases of life-threatening sepsis. In Australia, two life-threatening septic transfusion reactions (1.923 per million) were reported out of almost 1.04 million ELV bacterial culture-screened PCs, and no septic fatalities were reported. Meanwhile, in Canada, four life-threatening septic transfusion reactions (3.6/million) and one fatality (0.9/million) were observed in about 1.11 million ESV bacterial culture-screened PCs. CONCLUSION: DLV bacterial culture and PI treatment significantly reduced the incidence of septic reactions. The advantages and disadvantages of both systems merit further investigation before implementation.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".