Prevention of transfusion-transmitted infections
Bibliographic record
Abstract
Abstract Since the 1970s, introduction of serological assays targeting virus-specific antibodies and antigens has been effective in identifying blood donations infected with the classic transfusion-transmitted infectious agents (TTIs; hepatitis B virus [HBV], HIV, human T-cell lymphotropic virus types I and II, hepatitis C virus [HCV]). Subsequently, progressive implementation of nucleic acid–amplification technology (NAT) screening for HIV, HCV, and HBV has reduced the residual risk of infectious-window-period donations, such that per unit risks are <1 in 1 000 000 in the United States, other high-income countries, and in high-incidence regions performing NAT. NAT screening has emerged as the preferred option for detection of newer TTIs including West Nile virus, Zika virus (ZIKV), and Babesia microti. Although there is continual need to monitor current risks due to established TTI, ongoing challenges in blood safety relate primarily to surveillance for emerging agents coupled with development of rapid response mechanisms when such agents are identified. Recent progress in development and implementation of pathogen-reduction technologies (PRTs) provide the opportunity for proactive rather than reactive response to blood-safety threats. Risk-based decision-making tools and cost-effectiveness models have proved useful to quantify infectious risks and place new interventions in context. However, as evidenced by the 2015 to 2017 ZIKV pandemic, a level of tolerable risk has yet to be defined in such a way that conflicting factors (eg, theoretical recipient risk, blood availability, cost, and commercial interests) can be reconciled. A unified approach to TTIs is needed, whereby novel tests and PRTs replace, rather than add to, existing interventions, thereby ameliorating cost and logistical burden to blood centers and hospitals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".