Monitoring and preserving diaphragmatic function in mechanical ventilation
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes the evidence on clinical outcomes related to diaphragm dysfunction, providing an overview on available monitoring tools and strategies for its prevention and treatment. RECENT FINDINGS: Long-term adverse functional outcomes in intensive care survivors are well documented, especially in patients with prolonged mechanical ventilation. Because diaphragm weakness is highly prevalent and strongly associated with weaning failure, a link between diaphragm weakness and adverse functional outcomes is probable. Mechanisms of critical illness-associated diaphragm weakness are complex and include ventilator-related myotrauma through various pathways (i.e. over-assistance, under-assistance, eccentric, expiratory). Given this potential clinical impact, research on preventive and therapeutic strategies is growing, including the development of ventilation strategies aiming at protecting both the lung and the diaphragm. Phrenic nerve stimulation and specific rehabilitation strategies also appear promising. SUMMARY: Diaphragm dysfunction is associated with adverse clinical outcomes in ventilated patients; therefore, their inspiratory effort and function should be monitored. Whenever possible, and without compromising lung protection, moderate inspiratory effort should be targeted. Phrenic nerve stimulation and specific rehabilitation strategies are promising to prevent and treat diaphragm dysfunction, but further evidence is needed before widespread 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 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".