Monitoring patient–ventilator asynchrony
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article describes and discusses the importance of monitoring patient-ventilator asynchrony, and the advantages and limitations of the specific techniques available at the bedside to evaluate it. RECENT FINDINGS: Signals provided by esophageal catheters (pressure or electromyogram) are the most reliable and accurate instruments to detect asynchronies. Esophageal signals (providing electrical activity of the diaphragm or/and esophageal pressure) have allowed the recent description of reverse triggering, a new kind of asynchrony, in which mechanical insufflation repeatedly triggers diaphragmatic contractions. However, the use of esophageal catheters is not widespread, and data on the prevalence and consequences of asynchronies are still scarce. The development of software solutions that continuously and automatically record breathing waveforms from the ventilator recording is emerging. Using this technology, recent data support the fact that asynchronies are frequent and may be negatively associated with outcome. SUMMARY: The prevalence and consequences of asynchronies may be largely underestimated because of a frequent lack of monitoring. Dedicated software solutions that continuously and automatically detect asynchronies may allow both clinical research and clinical applications aimed at determining the effects of asynchronies and minimizing their incidence among critically ill patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".