Red Blood Cell Transfusion in Critically Ill Adults
Bibliographic record
Abstract
BACKGROUND: Blood products frequently are administered to critically ill patients. Considering recent trials and practice variability, a comprehensive review of current evidence was deemed essential to offer pertinent guidance to critical care practitioners. This American College of Chest Physicians (CHEST) guidelines panel examined the literature on RBC transfusions among critically ill patients overall and specific subgroups, including patients with gastrointestinal bleeding, acute coronary syndrome (ACS), cardiac surgery, isolated troponin elevation, and septic shock, to provide evidence-based recommendations. STUDY DESIGN AND METHODS: A panel of experts developed six Population, Intervention, Comparator, and Outcome questions addressing RBC transfusions in critically ill patients and performed a comprehensive evidence review. The panel applied the Grading of Recommendations, Assessment, Development, and Evaluations approach to assess the certainty of evidence and to formulate and grade recommendations. A modified Delphi technique was used to reach consensus on the recommendations. RESULTS: The initial search identified a total of 3,082 studies, and after the initial screening, 38 articles were reviewed. Among them, 23 studies met inclusion criteria, comprising 22 randomized controlled trials and one cohort study. Based on the analysis of these studies, the panel formulated two strong and four conditional recommendations. The overall quality of evidence for recommendations ranged from very low to moderate. CONCLUSIONS: In most critically ill patients, a restrictive strategy was preferable to a permissive approach because it does not increase the risk of death or complications, but does decrease RBC use significantly. Data from critically ill subpopulations also supported a restrictive approach, except in patients with ACS, for whom favoring a restrictive approach could increase adverse outcomes.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".