Transfusion-transmitted <i>Babesia</i> spp.: a changing landscape of epidemiology, regulation, and risk mitigation
Bibliographic record
Abstract
ABSTRACT Babesia spp. are tick-borne parasites with a global distribution and diversity of vertebrate hosts. Over the next several decades, climate change is expected to impact humans, vectors, and vertebrate hosts and change the epidemiology of Babesia . Although humans are dead-end hosts for tick-transmitted Babesia , human-to-human transmission of Babesia spp. from transfusion of red blood cells and whole blood-derived platelet concentrates has been reported. In most patients, transfusion-transmitted Babesia (TTB) results in a moderate-to-severe illness. Currently, in North America, most cases of TTB have been described in the United States. TTB cases outside North America are rare, but case numbers may change over time with increased recognition of babesiosis and as the epidemiology of Babesia is impacted by climate change. Therefore, TTB is a concern of microbiologists working in blood operator settings, as well as in clinical settings where transfusion occurs. Microbiologists play an important role in deploying blood donor screening assays in Babesia endemic regions, identifying changing risks for Babesia in non-endemic areas, investigating recipients of blood products for TTB, and drafting TTB policies and guidelines. In this review, we provide an overview of the clinical presentation and epidemiology of TTB. We identify approaches and technologies to reduce the risk of collecting blood products from Babesia -infected donors and describe how investigations of TTB are undertaken. We also describe how microbiologists in Babesia non-endemic regions can assess for changing risks of TTB and decide when to focus on laboratory-test-based approaches or pathogen reduction to reduce TTB risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".