Dyspnoea in acutely ill mechanically ventilated adult patients: an ERS/ESICM statement
Bibliographic record
Abstract
This statement outlines a review of the literature and current practice concerning the prevalence, clinical significance, diagnosis and management of dyspnoea in critically ill, mechanically ventilated adult patients. It covers the definition, pathophysiology, epidemiology, short- and middle-term impact, detection and quantification, and prevention and treatment of dyspnoea. It represents a collaboration of the European Respiratory Society and the European Society of Intensive Care Medicine. Dyspnoea ranks among the most distressing experiences that human beings can endure. Approximately 40% of patients undergoing invasive mechanical ventilation in the intensive care unit (ICU) report dyspnoea, with an average intensity of 45 mm on a visual analogue scale from 0 to 100 mm. Although it shares many similarities with pain, dyspnoea can be far worse than pain in that it summons a primal fear response. As such, it merits universal and specific consideration. Dyspnoea must be identified, prevented and relieved in every patient. In the ICU, mechanically ventilated patients are at high risk of experiencing breathing difficulties because of their physiological status and, in some instances, because of mechanical ventilation itself. At the same time, mechanically ventilated patients have barriers to signalling their distress. Addressing this major clinical challenge mandates teaching and training, and involves ICU caregivers and patients. This is even more important because, as opposed to pain which has become a universal healthcare concern, very little attention has been paid to the identification and management of respiratory suffering in mechanically ventilated ICU patients.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".