The international experience of bacterial screen testing of platelet components with automated microbial detection systems: An update
Bibliographic record
Abstract
In 2014, the bacterial subgroup of the Transfusion-Transmitted Infectious Diseases working party of ISBT published a review on the International Experience of Bacterial Screen Testing of Platelet Components (PCs) with an Automated Microbial Detection System. The purpose of this review, which is focused on publications on or after 2014, is to summarize recent experiences related to bacterial contamination of PCs and the use of an automated culture method to safeguard the blood supply. We first reviewed septic transfusion reactions after PC transfusion as reported in national haemovigilance systems along with a few reports from various countries on bacterial contamination of blood products. Next, we reviewed PC automated culture protocols employed by national blood services in the United Kingdom, Australia, Canada and large blood collection organization and hospital transfusion services in the United States. Then, we acknowledged the limitations of currently available culture methodologies in abating the risks of transfusion-transmitted bacterial infection, through a review of case reports. This review was neither meant to be critical of the literature reviewed nor meant to identify or recommend a best practice. We concluded that significant risk reduction can be achieved by one or a combination of more than one strategy. No one approach is feasible for all institutions worldwide. In selecting strategies, institutions should consider the possible impact on platelet components availability and entertain a risk-based decision-making approach that accounts for operational, logistical and financial factors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".