Assessing the risk of <i>Babesia</i> to the United States blood supply using a risk‐based decision‐making approach: Report of AABB's Ad Hoc <i>Babesia</i> Policy Working Group (original report)
Bibliographic record
Abstract
Recognizing the increasing threat of transfusion-transmitted babesiosis to the US blood supply, the AABB Board of Directors tasked an Ad Hoc Babesia Policy Working Group (the Working Group) to use the Alliance of Blood Operators' risk-based decision-making (RBDM) framework to assess the risks and benefits of introducing Babesia donation testing in the United States. The regional nature of the Babesia microti risk added complexity to the RBDM assessment because of the unique operational and financial considerations for operators and hospitals located in endemic states. Therefore, the assessment considered safety, product availability, sector sustainability, and technology availability. After assessing safety risk, economic and operational impact, reimbursement equity, ethical considerations, and stakeholder feedback from two consultations, the Working Group concluded that a regional approach to donor screening in endemic states was appropriate because it applied the intervention where the risk was highest and appropriately allocated cost to the risk. Nucleic acid testing using a ribosomal RNA template was the recommended intervention because it was the most cost-effective, resulted in no wasted units, and captured similar numbers of infections as antibody plus DNA-based polymerase chain reaction. The current model for blood reimbursement was maintained but AABB was encouraged to facilitate collection of data to identify threats to sector sustainability in endemic states. Babesia expansion was acknowledged with a mechanism to regularly reevaluate what are "endemic states." Finally, given that public awareness of the Babesia threat is the first line of defense, AABB should work with appropriate agencies for general education about the health risk from B. microti.
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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 | 0.062 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".