Bibliographic record
Abstract
PURPOSE OF REVIEW: Perioperative transfusion thresholds in the neurosurgical patient are undefined. Many neurosurgical procedures are associated with significant risk of bleeding. This review will summarize the current understanding of blood transfusion in the neurosurgical patient, as well as other blood component therapies and blood conservation strategies. RECENT FINDINGS: Transfusion of red blood cells has been demonstrated to improve cerebral oxygen delivery. Clinical studies on transfusion-related morbidity and mortality in the neurosurgical patient are limited. Recent findings in both subarachnoid hemorrhage and traumatic brain injured patients have shown worse outcomes in patients with anemia (Hb <9.0 g/dl) yet transfusion of red blood cells may not be associated with improved outcome. SUMMARY: Perioperative transfusion management for intracranial neurosurgical procedures presents the clinician with multiple challenges. Clinical evidence is sparse with view to an optimal hemoglobin level, yet anemia is known to be a predictor of poor outcome in many neurosurgical patients. Transfusion thresholds from other patient populations may not apply to this group and further prospective investigations are desperately needed. Until then, clinicians should focus on an individualized assessment of anemia tolerance, consider blood conservation strategies and understand the potential risks and benefits of blood 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".