Canadian experience with detection of bacterial contamination in apheresis platelets
Bibliographic record
Abstract
BACKGROUND: In Canada, both blood suppliers, Héma-Québec (HQ) and Canadian Blood Services (CBS), implemented bacterial testing in apheresis platelets (PLTs) with an automated microbial detection system (BacT/ALERT, bioMérieux). STUDY DESIGN AND METHODS: Validation of the BacT/ALERT Classic and 3D systems involved apheresis PLT spiking with different bacteria at concentrations of 10 and 10(2) colony-forming units per mL. As of February 2006, more than 95 percent of apheresis PLTs were screened for bacterial contamination at HQ and CBS. Between 3.5 and 10 mL of PLTs is inoculated into BacT/ALERT aerobic culture bottles followed by incubation for a maximum of 7 days. RESULTS: During the validation studies, all bacteria were detected at all concentrations and volumes tested. Upon implementation of bacterial screening, the percentage of initial positive samples at CBS and HQ was 0.09 and 0.07 percent, respectively. The rate of indeterminate cultures was significantly higher at CBS than at HQ, whereas the rates for true-positive, false-positive, and false-negative results did not differ significantly. Six confirmed-positive cultures, including three coagulase-negative staphylococci and three Enterobacteriaceae species, were detected and PLT units contaminated with these bacteria were not transfused. The rate of true-positive cultures was significantly lower than that reported by other blood operators. Unfortunately, failed detection of two contaminated units resulted in septic transfusion reactions. CONCLUSION: Bacterial screening of apheresis PLTs in Canada was successfully implemented, and transfusion of contaminated units was prevented. Rapid bacterial detection systems that could be used before transfusion, however, may further reduce the risk of transfusion reactions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".