Ventilatory targets following brain injury
Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".