Transfusion practices in traumatic brain injury
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this review is to summarize the recent studies looking at the effects of anemia and red blood cell transfusion in critically-ill patients with traumatic brain injury (TBI), describe the transfusion practice variations observed worldwide, and outline the ongoing trials evaluating restrictive versus liberal transfusion strategies for TBI. RECENT FINDINGS: Anemia is common among critically-ill patients with TBI, it is also thought to exacerbate secondary brain injury, and is associated with an increased risk of poor outcome. Conversely, allogenic red blood cell transfusion carries its own risks and complications, and has been associated with worse outcomes. Globally, there are large reported differences in the hemoglobin threshold used for transfusion after TBI. Observational studies have shown differential results for improvements in cerebral oxygenation and metabolism after red blood cell transfusion in TBI. SUMMARY: Currently, there is insufficient evidence to make strong recommendations regarding which hemoglobin threshold to use as a transfusion trigger in critically-ill patients with TBI. There is also uncertainty whether the restrictive transfusion strategy used in general critical care can be extrapolated to acutely brain injured patients. Ultimately, the consequences of anemia-induced cerebral injury need to be weighed up against the risks and complications associated with red blood cell transfusion.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".