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Record W2902703195 · doi:10.1097/ccm.0000000000003558

Impact of Altered Airway Pressure on Intracranial Pressure, Perfusion, and Oxygenation: A Narrative Review

2018· review· en· W2902703195 on OpenAlexafffund
Han Chen, David K. Menon, Brian P. Kavanagh

Bibliographic record

VenueCritical Care Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaNational Institute for Health and Care Research
KeywordsMedicineCerebral perfusion pressureIntracranial pressureOxygenationAnesthesiaIntensive careAirwayVentilation (architecture)Intensive care medicineCardiologyCerebral blood flow

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.050
GPT teacher head0.402
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations40
Published2018
Admission routes2
Has abstractyes

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