Transfusion in critical care: Past, present and future
Bibliographic record
Abstract
Anaemia and coagulopathy are common in critically ill patients and are associated with poor outcomes, including increased risk of mortality, myocardial infarction, failure to be liberated from mechanical ventilation and poor physical recovery. Transfusion of blood and blood products remains the corner stone of anaemia and coagulopathy treatment in critical care. However, determining when the benefits of transfusion outweigh the risks of anaemia may be challenging in some critically ill patients. Therefore, the European Society of Intensive Care Medicine prioritised the development of a clinical practice guideline to address anaemia and coagulopathy in non-bleeding critically ill patients. The aims of this article are to: (1) review the evolution of transfusion practice in critical care and the direction for future developments in this important area of transfusion medicine and (2) to provide a brief synopsis of the guideline development process and recommendations in a format designed for busy clinicians and blood bank staff. These clinical practice guidelines provide recommendations to clinicians on how best to manage non-bleeding critically ill patients at the bedside. More research is needed on alternative transfusion targets, use of transfusions in special populations (e.g., acute neurological injury, acute coronary syndromes), use of anaemia prevention strategies and point-of-care interventions to guide transfusion strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".