Bibliographic record
Abstract
BACKGROUND: Anemia is a common complication of critical illness. Because tissue hypoxia is a prominent factor in the development of organ dysfunction in the critically ill, conventional wisdom has argued that the transfusion of packed red blood cells can attenuate tissue hypoxia and so improve outcome. METHODS: Review of pertinent English-language literature. RESULTS: The empiric evidence supporting the benefit of transfusion to treat tissue hypoxia is sparse; indeed, a body of recent work suggests that moderate anemia is not only well-tolerated by the critically ill patient, it is associated with improved clinical outcomes. The primary biologic rationale for transfusion of the critically ill is to maximize oxygen delivery to tissues. However, because of reflex compensatory mechanisms, and because of alterations in microvascular flow and endothelial permeability, the impact of transfusion is much less than would be predicted. Retrospective studies suggest that transfusion is immunosuppressive, and associated with an enhanced infectious risk. The large Transfusion Requirements in Critical Care (TRICC) Trial conducted by the Canadian Critical Care Trials Group demonstrated reduced mortality and organ dysfunction when a transfusion trigger of 7 g/dL is used, without an increase in infectious complications. CONCLUSIONS: A conservative transfusion strategy appears safe in nearly all critically ill patients without active hemorrhage, including patients with cardiovascular disease. Whether a lower transfusion threshold could be adopted is unknown.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".