The impact of the coronavirus pandemic on sedation in critical care: volatile anesthetics in the ICU
Bibliographic record
Abstract
PURPOSE OF REVIEW: To reflect on the impact of the coronavirus pandemic on sedation for mechanically ventilated patients. RECENT FINDINGS: Shortages of intravenous sedatives during coronavirus pandemic renewed interest in using widely available inhaled anaesthetics for sedation of critically ill patients. Universally used for surgical anaesthesia, inhaled anaesthetics may offer therapeutic advantages in patients with acute lung injury with good sedation profiles, rapid clearance and lower lung inflammation in pilot trials. However, enabling ICU sedation with inhaled anaesthetics required technological and human resource innovation during the chaos of the global pandemic. The disruption of standard sedation practices is challenging during normal operations, yet pandemic facilitated innovation in this field by fostering cross-discipline collaboration supported by healthcare professionals, hospitals, research institutes and regulators. SUMMARY: Although further research is needed to establish the role of inhaled anaesthetics in critical care sedation toolkit, maintaining the spirit of innovation ignited during the recent coronavirus pandemic would require ongoing collaboration and streamlining of processes among healthcare, research and regulatory institutions.
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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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".