Variation in the Management of Pain, Agitation, and Delirium in Intensive Care Units in British Columbia
Bibliographic record
Abstract
BACKGROUND: Pain, agitation, and delirium are associated with negative outcomes in critically ill patients. Reducing variation in pain, agitation, and delirium management among institutions could improve care. OBJECTIVES: To define opportunities to improve pain, agitation, and delirium management in intensive care units in British Columbia, Canada. METHODS: A 13-item survey was developed to determine practices for assessing and managing pain, agitation, and delirium. Target participants were persons designated as the most informed about pain, agitation, and delirium management at each of the 30 intensive care units in British Columbia. Main measures were protocol use, assessment tool(s) used and frequency, and management approaches. RESULTS: All 30 units responded; half of them had a unit-specific pain algorithm. The Behavioral Pain Scale and the numerical rating scale were the most common tools used to assess pain. Sites reported 15 different approaches to pain management: two-thirds used a sedation assessment tool, but some relied on physician diagnoses to identify sedation. Sites reported 18 different approaches to sedation management: most included an algorithm or order set for sedation management, but the most commonly used approach was individualized management by a clinician (17% for sedation and 30% for agitation). Sites reported 22 different approaches for delirium management: more than two-thirds used a delirium measurement instrument, but some relied on physician diagnoses to identify delirium. CONCLUSION: Variation in assessment and management of pain, agitation, and delirium in British Columbia intensive care units highlights opportunities to improve care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".