Current status of rapid bacterial detection methods for platelet components: A 20‐year review by the <scp>ISBT</scp> Transfusion‐Transmitted Infectious Diseases Working Party Subgroup on Bacteria
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Bacterial contamination of platelet components (PCs) poses a safety challenge for transfusion patients. Despite mitigation interventions, the residual risk of transfusion-transmitted bacterial infections remains predominant. PC safety can be improved either by pathogen reduction or by implementation of bacterial detection methods. Detection methodologies include culture methods and rapid detection methods. The current review focuses on currently available rapid detection methods. MATERIALS AND METHODS: We reviewed published manuscripts since 2000 on rapid bacterial detection methods used for PC screening with result determination within 4 h. Methods meeting this criterion included Verax PGDprime, BacTx and nucleic amplification testing. The analytical and diagnostic sensitivity and specificity of these systems were assessed. RESULTS: The analytical sensitivity between the different detection methods ranged between 50 and 100,000 CFU/ml. The sample volume used by these testing systems varies between 0.5 and 1.0 ml of PCs. A delay of at least 48 h before sampling enhances detectability. All rapid detection methods generate results in a timely manner, allowing testing to be performed before transfusion with optimal sensitivity. CONCLUSION: Rapid detection methods improve PC safety regarding bacterial contamination. The assays are optimal for rapidly growing bacteria, which are more likely to cause septic transfusion reactions in patients. Because of the reduced diagnostic sensitivity, the sample collection should be late in shelf-life and ideally just before transfusion. The major benefit of these methods is that the test result can be obtained before releasing PCs for transfusion or to be used in combination with other screening methods applied early during PC storage.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".