Perioperative Ventilation in Neurosurgical Patients: Considerations and Challenges
Bibliographic record
Abstract
Abstract Purpose of Review The aim of this narrative review is to summarize critical considerations for perioperative airway management and mechanical ventilation in patients undergoing neurosurgical procedures. Recent Findings Given the significant influence that ventilation has on intracranial pressure (ICP) and cerebral blood flow, ventilator settings need to be carefully managed. For example, high positive end-expiratory pressure (PEEP) can increase ICP, while hyperventilation can reduce it. Finding the optimal balance is the key. While evidence supporting lung-protective ventilation in neurosurgical patients is limited, preliminary data suggest that its use could be beneficial, similar to general surgical patients. This typically involves using lower tidal volumes and maintaining optimal oxygenation to prevent ventilator-associated lung injury. Airway management in neurosurgical patients must consider the risk of increased ICP during intubation and the potential for airway complications. Techniques like rapid sequence induction and the use of neuromuscular blockers may be employed to minimize these risks. The primary goal of ventilation in neurosurgical patients is to maintain adequate oxygenation and carbon dioxide removal while minimizing harm to the lungs and brain. However, there may be exceptions where specific ventilatory adjustments are needed, such as in cases of compromised gas exchange or elevated ICP. Summary Patients undergoing neurosurgical procedures often require invasive ventilation due to the complexities of the operation and the need to manage the airway. This creates unique challenges because ventilator settings must balance the need to protect both the lungs and the brain. Further research is needed to establish clear guidelines and optimize ventilatory care in this population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".