Optimizing sedation and analgesia in mechanically ventilated patients--an evidence-based approach.
Bibliographic record
Abstract
Critically ill, mechanically ventilated patients experience pain and anxiety related to a number of factors, including underlying disease processes, invasive procedures, therapeutic devices, immobility, and even routine nursing care such as turning and positioning. Failure to provide adequate analgesia and sedation has been shown to have detrimental physiological consequences, including an increase in sympathetic nervous activity and ventilator dyssynchrony (Young, Knudsen, Hilton & Reves, 2000). Over-sedation has also given rise to concerns related to prolongation of mechanical ventilation, intensive care unit (ICU) length of stay, and cost. The challenge for the ICU team is to provide comfort while avoiding the consequences of both over- and under-sedation. New strategies show promise and focus on a team approach for the management of sedation and analgesia in critically ill, mechanically ventilated patients. These strategies include the use of sedation protocols, which incorporate nurse-driven dose titration directives, sedation scoring systems, and daily interruption of sedative infusions. This article provides a review of three recent studies evaluating these new approaches to the administration of sedation and analgesia in the adult ICU.
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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 | 0.004 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".