An international survey on measures to prevent transfusion‐transmitted infectious diseases—study results 1: Participation rates and the presence of laws, regulations, standards and best practices
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A global survey on blood safety measures to prevent transfusion-transmitted infectious diseases (TTIDs) was performed by examining variations in current usage. This analysis focuses on participation rates and the presence of relevant laws, regulations, standards and best practices for collection/processing of whole blood/components. MATERIALS AND METHODS: Distribution occurred between October 2023 and March 2024. States, provinces or cities within China and India were analysed as separate regions. Country/region (C/R) responses 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 multiple survey responses. RESULTS: Responses from 131 individuals representing 74 C/Rs (65 countries, Hong Kong, counted separately, and 8 regions in China/India) were analysed. Affirmative responses for laws, regulations and standards were similar across WBI levels. Regulatory jurisdiction for blood/components was present in 96% of C/Rs (HI 100%, UMI 100%, LLMI 87%) and 94% at a national level when present (HI 100%, UMI 94%, LLMI 85%). All HI, UMI and 74% LLMI C/Rs reported routinely separating whole blood into components. HI C/Rs were more likely to screen for bacterial contamination, whereas periodic platelet quality control was more common in LLMI and UMI C/Rs. Pathogen reduction and universal leukocyte reduction were more common in HI C/Rs. CONCLUSION: Laws, regulations and standards for collection/processing of blood were consistent across WBI groups. Resource-intensive practices of blood component separation and use of advanced blood safety technologies were more variable, with less utilization in LLMI/UMI C/Rs.
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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) | 0.003 | 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".