Residual risk of bacterial contamination of platelets: six years of experience with sterility testing
Bibliographic record
Abstract
BACKGROUND: Canadian Blood Services screens 100% of platelet concentrates (PCs) for bacterial contamination with the BacT/ALERT system. Quality-control sterility testing of 1% (≥10 units) of outdated PCs is performed monthly. Data from routine screening, quality-control testing, and septic reactions obtained from 2010 to 2016 are presented herein. STUDY DESIGN AND METHODS: In total, 601,988 buffy coat PC pools and 186,737 apheresis PCs were routinely screened with aerobic cultures over 6 years. Outdate quality-control testing of 8535 buffy coat and 8498 apheresis PCs was performed using aerobic and anaerobic cultures during the same period. Results were classified as "true-positives" when the same bacterium was isolated in initial and confirmatory cultures or "false-negatives" when bacteria were missed in early screening and were captured during quality-control sterility testing or through investigation of sepsis cases. RESULTS: During routine screening, the true-positive rates between buffy coat (0.94 per 10,000) and apheresis (0.96 per 10,000) PCs were similar (p = 0.9473). Seventy-five bacteria isolated during PC screening included Gram-positive and Gram-negative organisms. Six false-negative septic reactions were reported that implicated coagulase-negative staphylococci (n = 3) and Staphylococcus aureus (n = 3) for approximate rates of 1 per 100,000 transfusion reactions and 1 per 500,000 fatalities. During quality-control testing, the false-negative rates between buffy coat (8 per 10,000) and apheresis (9 per 10,000) PCs were similar (p = 0.7897). All 15 quality-control isolates were Gram-positive bacteria. CONCLUSION: The current bacterial screening protocol is efficacious for identifying Gram-negative bacteria. However, the high proportion of Gram-positive organisms detected on outdate quality-control testing and septic transfusion events demonstrates a residual safety risk that merits further intervention.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".