Evaluation of selective screening of donors for antibody to <scp><i>Trypanosoma cruzi</i></scp>: seroprevalence of donors who answer “no” to risk questions
Bibliographic record
Abstract
BACKGROUND: Selective testing of donors for Trypanosoma cruzi infection relies on identification of at-risk donors with screening questions. Using risk modeling and a seroprevalence study, we evaluated the risk of questions failing to identify T. cruzi antibody-positive donors. STUDY DESIGN AND METHODS: The rate of donors with unreported risk was estimated by a telephone survey of 2677 donors who answered "no" to risk questions. The number of T. cruzi antibody-positive donors missed by risk questions was estimated from the product of this rate and the selective testing T. cruzi antibody-positive rate. The 95% confidence interval (CI) was estimated by Monte Carlo simulation. To test the model, 60,132 donors were tested for T. cruzi antibody (26% of donors in selected regions, Phase I). In Winnipeg, Manitoba, the highest-risk region, 26,915 donors were tested (92.5% of donors, Phase II). RESULTS: In the telephone survey, 21 (0.8%) donors reported risk factors that would have identified them for selective testing. Seven were born in Mexico or Central or South America, five had travel risk, and nine had mother or maternal grandmother risk. The 95% CI for predicted number of T. cruzi antibody-positive donors answering "no" to risk questions was 0.71 to 4.38. In Phase I, one Winnipeg donor confirmed positive but had answered risk questions correctly. No other positive donations were identified. CONCLUSION: The estimated risk of T. cruzi-positive donors who answer "no" to risk questions is low and is confirmed by the seroprevalence among these donors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".