Blood Donation Testing and the Safety of the Blood Supply
Bibliographic record
Abstract
Many prescribers of transfusion now consider blood very safe for patients. Whilst it is impossible to provide a product for transfusion that is risk-free, all blood transfusion services follow a number of strategies aimed at minimizing risks associated with the transfusion of their product. Testing of blood donations focuses on two key areas: red cell serology (blood grouping) and microbiological screening. As these procedures for testing are typically applied to hundreds or thousands of donations in a day, operational and quality control issues are key to providing sufficient product ‘guarantees’. Blood grouping ensures that the risk of haemolysis due to immunological incompatibility is minimized. Similarly microbiological screening also ensures that the risk of transmissible infection is minimized. A number of steps apply to reduce risks of transfusion transmission of infection, including the application of donor selection criteria to defer individuals considered at higher risk of infection and screening tests to identify known pathogens. Policies need to be in place to notify and counsel donors with repeat (or confirmed) positive test results. The perceived current safety of blood for transfusion is a testament to the ongoing rigour of donor screening and blood donation testing.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".