Lung-protective ventilation in neurosurgical patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Concepts of ventilator-induced lung injury have revolutionized our approach to the ventilatory management of patients with acute lung injury and acute respiratory distress syndrome over the past 10 years. The extension of these principles to patients with brain injuries is challenging, as many of them are out of keeping with usual brain-protective management. RECENT FINDINGS: Many patients with acute lung injury or acute respiratory distress syndrome and an acute brain injury may in fact be managed safely within the confines of a lung-protective strategy. Elevated levels of positive end-expiratory pressure in head-injured patients with acute lung injury or acute respiratory distress syndrome also appear to be safe, particularly when the level is set below that of the intracranial pressure, when patients have a low respiratory system compliance, or when positive end-expiratory pressure results in significant lung volume recruitment. Several novel therapies to minimize ventilator-induced lung injury are currently in the early stages of investigation in neurosurgical patients. SUMMARY: In many patients with brain injuries and acute lung injury the goals of lung protection can be achieved without threatening cerebral perfusion. In patients with more refractory raised intracranial pressure the optimal balance between brain and lung is not well established. Further research is needed on lung-protective strategies in this vulnerable 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".