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Record W4319823650 · doi:10.1097/mcc.0000000000001018

Ventilatory targets following brain injury

2023· review· en· W4319823650 on OpenAlexaff
Shaurya Taran, Sarah Wahlster, Chiara Robba

Bibliographic record

VenueCurrent Opinion in Critical Care · 2023
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePlateau pressureCerebral perfusion pressureMechanical ventilationVentilation (architecture)Traumatic brain injuryPopulationTidal volumeIntensive care medicineAnesthesiaInternal medicineRespiratory systemCerebral blood flowPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Recent studies have focused on identifying optimal targets and strategies of mechanical ventilation in patients with acute brain injury (ABI). The present review will summarize these findings and provide practical guidance to titrate ventilatory settings at the bedside, with a focus on managing potential brain-lung conflicts. RECENT FINDINGS: Physiologic studies have elucidated the impact of low tidal volume ventilation and varying levels of positive end expiratory pressure on intracranial pressure and cerebral perfusion. Epidemiologic studies have reported the association of different thresholds of tidal volume, plateau pressure, driving pressure, mechanical power, and arterial oxygen and carbon dioxide concentrations with mortality and neurologic outcomes in patients with ABI. The data collectively make clear that injurious ventilation in this population is associated with worse outcomes; however, optimal ventilatory targets remain poorly defined. SUMMARY: Although direct data to guide mechanical ventilation in brain-injured patients is accumulating, the current evidence base remains limited. Ventilatory considerations in this population should be extrapolated from high-quality evidence in patients without brain injury - keeping in mind relevant effects on intracranial pressure and cerebral perfusion in patients with ABI and individualizing the chosen strategy to manage brain-lung conflicts where necessary.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.290
GPT teacher head0.508
Teacher spread0.217 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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