The International Society of Blood Transfusion (<scp>ISBT</scp>) Public Health Research Toolkit: A report from the Surveillance, Risk Assessment and Policy Sub‐group of the <scp>ISBT</scp> Transfusion Transmitted Infectious Diseases Working Party
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Data provided from blood donors have contributed to the understanding of public health epidemiology and policy decisions. A recent example was during the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) pandemic when blood services monitored the seroprevalence in blood donors. Based on this experience, blood services have the opportunity to expand their role and participate in public health surveillance and research. The aim of this report is to share available resources to assist blood services in this area. MATERIALS AND METHODS: The Surveillance, Risk Assessment and Policy (SRAP) Sub-group of the International Society of Blood Transfusion (ISBT) Transfusion Transmitted Infectious Diseases (TTID) Working Party developed a Public Health Research Toolkit to assist blood services and researchers interested in expanding their role in public health research. RESULTS: The ISBT Public Health Research Toolkit provides resources for what blood services can offer to public health, examples of donor research studies, the utility of donor data and website links to public health agencies. The toolkit includes a customizable template for those interested in establishing and managing a biobank. CONCLUSION: The ISBT Public Health Research Toolkit includes resources to increase the recognition of the role blood donors can play in public health and to help blood services gain commitment and funding from various agencies for new research and surveillance.
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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 | 0.119 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".