Implementing a bedside assessment of respiratory mechanics in patients with acute respiratory distress syndrome
Bibliographic record
Abstract
BACKGROUND: Despite their potential interest for clinical management, measurements of respiratory mechanics in patients with acute respiratory distress syndrome (ARDS) are seldom performed in routine practice. We introduced a systematic assessment of respiratory mechanics in our clinical practice. After the first year of clinical use, we retrospectively assessed whether these measurements had any influence on clinical management and physiological parameters associated with clinical outcomes by comparing their value before and after performing the test. METHODS: The respiratory mechanics assessment constituted a set of bedside measurements to determine passive lung and chest wall mechanics, response to positive end-expiratory pressure, and alveolar derecruitment. It was obtained early after ARDS diagnosis. The results were provided to the clinical team to be used at their own discretion. We compared ventilator settings and physiological variables before and after the test. The physiological endpoints were oxygenation index, dead space, and plateau and driving pressures. RESULTS: Sixty-one consecutive patients with ARDS were enrolled. Esophageal pressure was measured in 53 patients (86.9%). In 41 patients (67.2%), ventilator settings were changed after the measurements, often by reducing positive end-expiratory pressure or by switching pressure-targeted mode to volume-targeted mode. Following changes, the oxygenation index, airway plateau, and driving pressures were significantly improved, whereas the dead-space fraction remained unchanged. The oxygenation index continued to improve in the next 48 h. CONCLUSIONS: Implementing a systematic respiratory mechanics test leads to frequent individual adaptations of ventilator settings and allows improvement in oxygenation indexes and reduction of the risk of overdistention at the same time. TRIAL REGISTRATION: The present study involves data from our ongoing registry for respiratory mechanics (ClinicalTrials.gov identifier: NCT02623192 . Registered 30 July 2015).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".