Blood Donor Selection and Screening: Strategies to Reduce Recipient Risk
Bibliographic record
Abstract
Various measures are taken to ensure the safety of the blood supply. Donor selection begins with education of the public about transfusion-transmissible diseases. Potential donors must answer a questionnaire designed to identify specific risk factors for these infections. The questionnaire is the only line of protection against certain infections for which no testing is performed, such as malaria, babesiosis, leishmaniasis, and Chagas disease. All donations are tested for the presence of antibodies to HIV-1 and -2, HCV, HTLV and syphilis, the hepatitis B surface antigen (HbsAg), the p24 antigen (HIV), and also for HIV and HCV nucleic acids. The introduction of new and improved screening tests for transfusion-transmissible diseases has led to remarkable improvement in the safety of the blood supply, with substantial shortening of the window period for HIV, HCV, and HBV infections. The current challenge of the industry is to reduce even further the small but significant risk of bacterial contamination of platelet components. Finally, some safety measures are purely precautionary, such as the deferral of donors who have traveled to certain countries affected by the bovine spongiform encephalopathy (BSE).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".