Platelet transfusion in adults: An update
Bibliographic record
Abstract
Many patients worldwide receive platelet components (PCs) through the transfusion of diverse types of blood components. PC transfusions are essential for the treatment of central thrombocytopenia of diverse causes, and such treatment is beneficial in patients at risk of severe bleeding. PC transfusions account for almost 10% of all the blood components supplied by blood services, but they are associated with about 3.25 times as many severe reactions (attributable to transfusion) than red blood cell transfusions after stringent in-process leukoreduction to less than 106 residual cells per blood component. PCs are not homogeneous, due to the considerable differences between donors. Furthermore, the modes of PC collection and preparation, the safety precautions taken to limit either the most common (allergic-type reactions and febrile non-hemolytic reactions) or the most severe (bacterial contamination, pulmonary lesions) adverse reactions, and storage and conservation methods can all result in so-called PC “storage lesions”. Some storage lesions affect PC quality, with implications for patient outcome. Good transfusion practices should result in higher levels of platelet recovery and efficacy, and lower complication rates. These practices include a matching of tissue ABH antigens whenever possible, and of platelet HLA (and, to a lesser extent, HPA) antigens in immunization situations. This review provides an overview of all the available information relating to platelet transfusion, from donor and donation to bedside transfusion, and considers the impact of the measures applied to increase transfusion efficacy while improving safety and preventing transfusion inefficacy and refractoriness. It also considers alternatives to platelet component (PC) transfusion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 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; both teacher heads agree on what is shown here.
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".