A congress report of the new Emerging Pathogens and Parasitology Subgroup within the <scp>ISBT</scp> Working Party on Transfusion‐Transmitted Infectious Diseases
Bibliographic record
Abstract
In 2025, the Parasitology Subgroup of the International Society of Blood Transfusion (ISBT) Transfusion-Transmitted Infectious Diseases (TTID) Working Party (WP) transitioned into the Emerging Pathogens and Parasitology (EPP) Subgroup (referred to here as the EPP). This followed recognition that the parasitology subgroup's relevance was limited in scope given the small number of transfusion-transmissible parasites that still lacked effective mitigation. The EPP was proposed to address themes that are not adequately covered by existent subgroups of the TTID WP. In addition to maintaining a focus on transfusion-transmissible parasitic infections, a major objective of the EPP is horizon scanning for emerging pathogens. Horizon scanning refers to a systematic and proactive approach of information gathering and evaluation to identify early-and often subtle-signals of possible threats, which in this case pertain to blood safety. The EPP will characterize those risks to guide decision making and preparedness, pertaining to the safety and sufficiency of the blood supply. We describe the scope, structure and functioning of the EPP, within the broader TTID WP. We include examples of projects that may be pursued and outputs from horizon scanning a contemporary emerging pathogen. This collectively highlights the strategic relevance and objectives of the EPP.
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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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".