Impact of Altered Airway Pressure on Intracranial Pressure, Perfusion, and Oxygenation: A Narrative Review
Bibliographic record
Abstract
OBJECTIVES: A narrative review of the pathophysiology linking altered airway pressure and intracranial pressure and cerebral oxygenation. DATA SOURCES: Online search of PubMed and manual review of articles (laboratory and patient studies) of the altered airway pressure on intracranial pressure, cerebral perfusion, or cerebral oxygenation. STUDY SELECTION: Randomized trials, observational and physiologic studies. DATA EXTRACTION: Our group determined by consensus which resources would best inform this review. DATA SYNTHESIS: In the normal brain, positive-pressure ventilation does not significantly alter intracranial pressure, cerebral oxygenation, or perfusion. In injured brains, the impact of airway pressure on intracranial pressure is variable and determined by several factors; a cerebral venous Starling resistor explains much of the variability. Negative-pressure ventilation can improve cerebral perfusion and oxygenation and reduce intracranial pressure in experimental models, but data are limited, and mechanisms and clinical benefit remain uncertain. CONCLUSIONS: The effects of airway pressure and ventilation on cerebral perfusion and oxygenation are increasingly understood, especially in the setting of brain injury. In the face of competing mechanisms and priorities, multimodal monitoring and individualized titration will increasingly be required to optimize care.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".