Secondary bacterial culture of platelets to mitigate transfusion‐associated sepsis: A 3‐year analysis at a large academic institution
Bibliographic record
Abstract
BACKGROUND: In 2019, the United States Food and Drug Administration published its final recommendations to mitigate bacterial contamination of platelets. We sought to evaluate our secondary bacterial culture (SBC) strategy in light of those recommendations. STUDY DESIGN AND METHODS: A retrospective analysis was conducted of SBC data (October 2016-2019) at our institution. SBC was performed upon receipt (Day 3 after collection); 5 mL of platelet product was inoculated aseptically into an aerobic bottle and incubated at 35°C for 3 days. For 8 months, a 10-mL inoculum was trialed. No quarantine was applied. All positive cultures underwent Gram staining and repeat culture of the platelet product (if available). A probable true positive was defined as concordant positive culture between the initial and repeat culture. The incidence of probable true- and false-positive cultures were reported descriptively and differences evaluated by sampling volume. RESULTS: Over 3 years, 55 896 platelet products underwent SBC, yielding 30 initial positive results (approx. 1/1863 platelets); 25 (83.3%) signaled within 24 hours of SBC. The rates of probable true positive, false positive, and indeterminate for 5 mL were 0.027% (1/3771), 0.002% (1/45 251) and 0.018% (1/5656), respectively. The respective rates for 10 mL were 0.018% (1/5323), 0.07% (1/1521), and 0%. Seven of eight (87.5%) false-positive SBCs occurred with a 10-mL inoculum. No septic transfusion reactions were reported. CONCLUSION: SBC continues to interdict bacterially contaminated units of platelets. Our findings suggest higher rates of false positivity using large-volume inocula.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".