Incidence and significance of the bacterial contamination of blood components.
Bibliographic record
Abstract
A septic reaction occurring during or following the transfusion of cellular blood components was one of the earliest recognized complications of allogeneic blood transfusions. The presence of bacteria in blood products has been a problem for many decades and currently it is probably the most common microbiological cause of transfusion-associated morbidity and mortality. Transfusion-associated sepsis due to contaminated platelet concentrates appears to be much more common than those due to red cells. The overall prevalence of contaminated cellular blood products (red cells and platelets) is approximately one in 3000; however, the transfusion to a recipient of a contaminated blood product may not necessarily be associated with clinically evident morbidity. This is because the majority of contaminated blood product units contain only few bacteria. In other instances, contaminated units may contain large numbers of virulent bacteria as well as endotoxins, and their transfusion may be associated with significant morbidity and even be lethal to the recipient. The prevalence of severe episodes of transfusion-associated sepsis has not been clearly established, but is probably of the order of one in 50,000 per platelet unit and one in 500,000 per red cell unit transfused. As a result of the increased recognition that such transfusion-associated episodes can occur, a variety of measures have been proposed to try to prevent and/or control the risk of transfusion-associated septic reactions.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".