Mechanical ventilation in patients with acute respiratory distress syndrome: current status and future perspectives
Bibliographic record
Abstract
INTRODUCTION: Although there has been extensive research on mechanical ventilation for acute respiratory distress syndrome (ARDS), treatment remains mainly supportive. Recent studies and new ventilatory modes have been proposed to manage patients with ARDS; however, the clinical impact of these strategies remains uncertain and not clearly supported by guidelines. The aim of this narrative review is to provide an overview and update on ventilatory management for patients with ARDS. AREAS COVERED: This article reviews the literature regarding mechanical ventilation in ARDS. A comprehensive overview of the principal settings for the ventilator parameters involved is provided as well as a report on the differences between controlled and assisted ventilation. Additionally, new modes of assisted ventilation are presented and discussed. The evidence concerning rescue strategies, including recruitment maneuvers and extracorporeal membrane oxygenation support, is analyzed. PubMed, EBSCO, and the Cochrane Library were searched up until June 2023, for relevant literature. EXPERT OPINION: Available evidence for mechanical ventilation in cases of ARDS suggests the use of a personalized mechanical ventilation strategy. Although promising, new modes of assisted mechanical ventilation are still under investigation and guidelines do not recommend rescue strategies as the standard of care. Further research on this topic is required.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".