Emerging infectious disease agents and their potential threat to transfusion safety
Bibliographic record
Abstract
BACKGROUND: Emerging infections have been identified as a continuing threat to human health. Many such infections are known to be transmissible by blood transfusion, while others have properties indicating this potential. There has been no comprehensive review of such infectious agents and their threat to transfusion recipient safety to date. STUDY DESIGN AND METHODS: The members of AABB's Transfusion Transmitted Diseases Committee reviewed a large number of information sources in order to identify infectious agents with actual or potential risk of transfusion transmission now or in the future in the US or Canada; with few exceptions, these agents do not have available interventions to reduce the risk of such transmission. Using a group discussion and writing process, key characteristics of each agent were identified, researched, recorded and documented in standardized format. A group process was used to prioritize each agent on the basis of scientific/epidemiologic data and a subjective assessment of public perception and/or concern expressed by regulatory agencies. RESULTS: Sixty-eight infectious agents were identified and are described in detail in a single Supplement to TRANSFUSION. Key information will also be provided in web-based form and updated as necessary. The highest priorities were assigned to Babesia species, Dengue virus, and vCJD. CONCLUSION: The information is expected to support the needs of clinicians and transfusion medicine experts in the recognition and management of emerging infections among blood donors and blood recipients.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".