Bacterial contamination in platelets: incremental improvements drive down but do not eliminate risk
Bibliographic record
Abstract
BACKGROUND: Bacterial contamination of platelet components (PCs) remains an important cause of transfusion-associated infectious risk. In 2004, Canadian Blood Services (CBS) implemented bacterial testing of PCs using the BacT/ALERT 3D system (bioMérieux). This system has been validated and implemented and continuous monitoring of culture rates allows gathering of data regarding true and false positives as well as false negatives. STUDY DESIGN AND METHODS: National data gathered between March 2004 and October 2010 from 12 CBS sites were analyzed to compare bacterial contamination rates across three platelet (PLT) preparation methods: apheresis, buffy coat, and PLT-rich plasma. Data were compared before and after implementation of protocol changes that may affect bacterial detection or contamination rates. RESULTS: Initial positive rates among the three production methods were significantly different, with apheresis PCs being the highest. The rates of confirmed positives among production methods did not differ significantly (p = 0.668). Increasing sample testing volumes from 4 to 6 mL to 8 to 10 mL significantly increased the rate of initial positives, while confirmed positives increased from 0.64 to 1.63 per 10,000, approaching significance (p = 0.055). Changing the skin disinfection method from a two-step to a one-step protocol did not significantly alter the rate of confirmed positives. During the period of data analysis, eight false-negative cases were reported, with five implicated in adverse transfusion reactions. CONCLUSION: Bacterial testing of PCs and implementation of improved protocols are incrementally effective in reducing the risk of transfusion of bacterially contaminated PLT concentrates; however, the continued occurrence of false-negative results means the risk has not been eliminated.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".