Detection of <i>Babesia</i><scp>RNA</scp> and <scp>DNA</scp> in whole blood samples from <scp>US</scp> blood donations
Bibliographic record
Abstract
BACKGROUND: Human babesiosis is a zoonotic infection caused by an intraerythrocytic parasite. The highest incidence of babesiosis is in the United States, although cases have been reported in other parts of the world. Due to concerns of transfusion-transmitted babesiosis, the US Food and Drug Administration (FDA) recommended year-round regional testing for Babesia by nucleic acid testing or use of an FDA-approved device for pathogen reduction. A new molecular test, cobas Babesia (Roche Molecular Systems, Inc.), was evaluated for the detection of the four species that cause human disease, Babesia microti, Babesia duncani, Babesia divergens, and Babesia venatorum. STUDY DESIGN AND METHODS: Analytical performance was evaluated followed by clinical studies on whole blood samples from US blood donations collected in a special tube containing a chaotropic reagent that lyses the red cells and preserves nucleic acid. Sensitivity and specificity of the test in individual samples (individual donation testing [IDT]) and in pools of six donations were determined. RESULTS: Based on analytical studies, the claimed limit of detection of cobas Babesia for B. microti is 6.1 infected red blood cells (iRBC)/mL (95% confidence interval [CI]: 5.0, 7.9); B. duncani was 50.2 iRBC/mL (95% CI: 44.2, 58.8); B. divergens was 26.1 (95% CI: 22.3, 31.8); and B. venatorum was 40.0 iRBC/mL (95% CI: 34.1, 48.7). The clinical specificity for IDT was 99.999% (95% CI: 99.996, 100) and 100% (95% CI: 99.987, 100) for pools of six donations. CONCLUSION: cobas Babesia enables donor screening for Babesia species with high sensitivity and specificity.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".