Bibliographic record
Abstract
PURPOSE OF REVIEW: The objective of this review is to identify outstanding topics most relevant to neuroanesthesia practice and patient outcomes. We discuss the role of awake craniotomy, choice of general anesthetic agents, monitoring of anesthetic 'depth', mannitol-induced diuresis, neurophysiological monitoring, hyperventilation, and cerebral hypoperfusion. RECENT FINDINGS: Awake craniotomy, although a technique likely underused, is associated with enhanced recovery after surgery and prolonged survival after brain tumor resection compared with surgery under general anesthesia. The choice of general anesthetic must balance patient and surgical factors. Although propofol may be associated with favorable oncologic outcomes, currently available retrospective evidence does not specifically address neurosurgical patients. Both the definition and monitoring of anesthetic 'depth' remains elusive. Neuroanesthesiologists need to recognize and manage intraoperative light anesthesia in a timely fashion. Further evidence related to the optimal management of mannitol-induced diuresis and hyperventilation in neurosurgical patients is needed. Contemporary neurophysiological monitoring can reasonably detect intraoperative neurologic injury; however, its effect on patient outcome is unclear. Finally, cerebral hypoperfusion without stroke may be common; however, the clinical significance requires further investigation. SUMMARY: We provide an overview of several topics that are relevant to neuroanesthesia practice and patient outcomes based on evidence, opinions, and speculations. Our review highlights the need for further outcome-oriented studies to specifically address these clinically relevant issues.
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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 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".