An international survey on measures to prevent transfusion‐transmitted infectious diseases—study results 2: Testing and donor vigilance
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The results of a global survey on blood safety measures to prevent transfusion-transmitted infectious diseases (TTIDs) were analysed, focusing on TTID testing requirements, practices and associated donor actions (vigilance). MATERIALS AND METHODS: Responses by country/region (C/R) were categorized by World Bank income (WBI) levels: low- and lower middle-income (LLMI), upper middle-income (UMI) and high-income (HI). Consensus responses were used for C/Rs with multiple survey responses. Regions within China and India were analysed separately. Survey questions on TTID testing and donor vigilance were compared across WBI levels. RESULTS: Responses from 74 C/Rs representing 65 countries and Hong Kong were analysed. All C/Rs reported mandatory human immunodeficiency virus (HIV), hepatitis B virus (HBV) and hepatitis C virus (HCV) screening, with a few exceptions for syphilis. Most testing standards were set by national law, using antibody and/or antigen testing for HIV, HBV and HCV; antibody testing was common for syphilis. Nucleic acid testing (NAT) was less common in LLMI than in UMI and HI C/Rs. Confirmatory testing was reported by almost all (96%) C/Rs, often using the same screening test. All C/Rs provided educational material to donors, and deferred/notified donors based on reactive/positive TTID results. Most C/Rs reported reactive/positive results to a central entity, and 89% withdrew and destroyed in-date units from previous collections. CONCLUSION: All reporting C/Rs screened for HIV, HBV and HCV, with most using confirmatory testing. Advanced tests such as NAT were less common in LLMI C/Rs. Donor vigilance was consistent across income groups, with education, notification and deferral for TTID results, and most reporting withdrawal/destruction of previous collections.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".