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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".