Modeling the risk of an emerging pathogen entering the Canadian blood supply
Bibliographic record
Abstract
BACKGROUND: As part of its risk management process, Canadian Blood Services (CBS) constructed mathematical models of how newly emerging pathogens might affect blood transfusion recipients. STUDY DESIGN AND METHODS: CBS convened an expert panel including medical, health economics, analytical, risk management, and insurance professionals to examine multiple data sources. The model for emerging pathogen risk included separate modules to calculate the frequency and severity of infections from transfusion-transmitted agents that could cause either acute transient or chronic persistent infection. Important model input variables were annual number of components transfused, the presumed incidence and prevalence of a new agent, the time interval of recipient risk, recipient age and sex, projected recipient survival, rate of secondary infection, pathogen-induced morbidity, and the associated medical costs of such morbidity. RESULTS: In the 5-year time frame considered in the model, it was estimated that approximately 3500 recipient infections (two-SD range of 0 to 11,370 infections) could occur from an emerging pathogen that establishes a chronic infection in donors, with 60% of these due to red blood cell transfusion. The medical costs associated with recipient outcomes due to a catastrophic emerging pathogen could be lowered by 20% if an effective pathogen reduction method for either platelets or plasma were in place. CONCLUSION: This modeling exercise offers a framework for other blood services to construct similar models. It also provides a useful way to model the implementation of new blood safety interventions (e.g., pathogen reduction) on emerging pathogen 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".