Why implement universal leukoreduction?
Bibliographic record
Abstract
The improvement of transfusion medicine technology is an ongoing process primarily directed at increasing the safety of allogeneic blood component transfusions for recipients. Over the years, relatively little attention had been paid to the leukocytes present in the various blood components. The availability of leukocyte removal (leukoreduction) techniques for blood components is associated with a considerable improvement in various clinical outcomes. These include a reduction in the frequency and severity of febrile transfusion reactions, reduced cytomegalovirus transfusion-transmission risk, the reduced incidence of alloimmune platelet refractoriness, a possible reduction in the risk of transfusion-associated variant Creutzfeldt-Jakob disease transmission, as well as reducing the overall risk of both recipient mortality and organ dysfunction, particularly in cardiac surgery patients and possibly in other categories of patients. Internationally, 19 countries have implemented universal leukocyte reduction (ULR) as part of their blood safety policy. The main reason for not implementing ULR in those countries that have not appears to be primarily concerns over costs. Nonetheless, the available international experience supports the concept that ULR is a process that results in improved safety of allogeneic blood components.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".