Use of hemovigilance data to evaluate the effectiveness of diversion and bacterial detection
Bibliographic record
Abstract
BACKGROUND: Several preventive measures, including diversion of the first aliquot of blood and culturing of platelet (PLT) components, have been implemented to decrease the risk of transfusion-transmitted bacterial infections (TTBIs). We evaluated the effectiveness of these measures in Québec using hemovigilance data from January 2000 to December 2008. STUDY DESIGN AND METHODS: Adverse transfusion reactions were reported to the Québec Ministry of Health by transfusion safety officers. Initial aliquot diversion, already in place for apheresis PLTs, was added to all whole blood collections in early 2003. Bacterial detection was implemented in March 2003 for apheresis PLTs and February 2005 for whole blood-derived PLTs (WBDPs). RESULTS: The incidence of probable and definite TTBIs associated with WBDPs decreased from 1 in 2655 to 1 in 27,737 five-unit pools (p = 0.004) after implementation of diversion. There were no reports of TTBIs with WBDPs after culture was added to diversion, further reducing the risk to 1 in 58,123 five-unit pools (p < 0.001). There was only one TTBI associated with apheresis PLTs during the 9-year period, which occurred after implementation of both diversion and culture. CONCLUSION: Hemovigilance data demonstrated a highly significant decrease in TTBIs associated with WBDPs, mainly attributed to the implementation of diversion. However, diversion and culture do not totally abolish the risk of TTBIs.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".