Techniques to monitor respiratory drive and inspiratory effort
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is increased awareness that derangements of respiratory drive and inspiratory effort are frequent and can result in lung and diaphragm injury together with dyspnea and sleep disturbances. This review aims to describe available techniques to monitor drive and effort. RECENT FINDINGS: Measuring drive and effort is necessary to quantify risk and implement strategies to minimize lung and the diaphragm injury by modifying sedation and ventilation. Evidence on the efficacy of such strategies is yet to be elucidated, but physiological and epidemiological data support the need to avoid injurious patterns of breathing effort.Some techniques have been used in research for decades (e.g., esophageal pressure or airway occlusion pressure), evidence on their practical utility is growing, and technical advances have eased implementation. More novel techniques (e.g., electrical activity of the diaphragm and ultrasound) are being investigated providing new insights on their use and interpretation. SUMMARY: Available techniques provide reliable measures of the intensity and timing of drive and effort. Simple, noninvasive techniques might be implemented in most patients and the more invasive or time-consuming in more complex patients at higher risk. We encourage clinicians to become familiar with technical details and physiological rationale of each for optimal implementation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".