Bibliographic record
Abstract
SUMMARY Transfusion reactions associated with bacteria and/or their products, during or following a blood transfusion, were one of the earliest recognized complications of allogeneic blood transfusions. Bacterial contamination of blood products has, therefore, been a problem for many decades and, at the present time, is likely the most common microbiological cause of transfusion‐associated mortality and morbidity. Septic reactions associated with platelet concentrates appear to be much more common than those associated with contaminated red blood cells. The overall prevalence of contaminated cellular blood products has been estimated to be approximately 1 in 3000; however, the transfusion of contaminated platelet or red blood cell units may not necessarily be associated with clinically apparent morbidity in recipients because the majority of contaminated blood product units contain relatively few organisms. Unfortunately, though, in a minority of instances, contaminated blood product units contain clinically significant numbers of bacteria as well as endotoxins that may be associated with significant mortality and/or morbidity. The incidence of clinically evident severe septic episodes has not yet been clearly established, but is probably in the order of 1 per 50,000 platelet units transfused and 1 per 500,000 red blood cell units transfused. In recent years, a variety of interventions have been proposed, and in some instances implemented, in an effort to reduce this significant and potentially preventable transfusion risk.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".