Monitoring and modulating respiratory drive in mechanically ventilated patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Respiratory drive is frequently deranged in the ICU, being associated with adverse clinical outcomes. Monitoring and modulating respiratory drive to prevent potentially injurious consequences merits attention. This review gives a general overview of the available monitoring tools and interventions to modulate drive. RECENT FINDINGS: Airway occlusion pressure (P0.1) is an excellent measure of drive and is displayed on ventilators. Respiratory drive can also be estimated based on the electrical activity of respiratory muscles and measures of respiratory effort; however, high respiratory drive might be present in the context of low effort with neuromuscular weakness. Modulating a deranged drive requires a multifaceted intervention, prioritizing treatment of the underlying cause and adjusting ventilator settings for comfort. Additional tools include changes in PEEP, peak inspiratory flow, fraction of inspired oxygen, and sweep gas flow (in patients receiving extracorporeal life-support). Sedatives and opioids have differential effects on drive according to drug category. Monitoring response to any intervention is warranted and modulating drive should not preclude readiness to wean assessment or delay ventilation liberation. SUMMARY: Monitoring and modulating respiratory drive are feasible based on physiological principles presented in this review. However, evidence arising from clinical trials will help determine precise thresholds and optimal interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| 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".