Methods for the detection of bacterial contamination in blood products
Bibliographic record
Abstract
Culture-based and molecular assays have been developed for the screening of platelet concentrates and other blood components for bacterial contaminations. In this review, the principles of the assays are outlined. The focus of this review is the assessment of the analytical qualities of the methods. Spiking studies by adding defined levels of a wide range of bacteria to the complex biological matrix provide the first basis to evaluate and compare the qualities of methods for bacterial detection. The sensitivity acceptable for reliable screening for bacteria critically depends on the timing of either early sampling (within a period of up to 24 h after preparation of the blood component) or late sampling (a few hours before issuing the blood component). Large screening studies are essential to confirm both adequate sensitivity and specificity of the testing. In the ideal setting, these studies are prospectively planned and include systematic surveillance of adverse events in response to the administration of the screened products. The findings from sterility testing (predominantly with automated systems for detection of bacteria based on CO(2) generation) of more than 550,000 platelet concentrates in 13 studies are summarised. The limitations of the early sampling and the "negative-to-date" strategy to issue platelet concentrates are addressed. A few reported cases of probable transmission of bacteria by platelet transfusion despite negative screening tests emphasise the need to further develop optimised methods for testing of bacteria blood components.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".