Assessment of Delirium in the Intensive Care Unit: Nursing Practices And Perceptions
Bibliographic record
Abstract
BACKGROUND: Despite practice guidelines promoting delirium assessment in intensive care, few data exist regarding current delirium assessment practices among nurses and how these practices compare with those for sedation assessment. OBJECTIVES: To identify current practices and perceptions of intensive care nurses regarding delirium assessment and to compare practices for assessing delirium with practices for assessing sedation. METHODS: A paper/Web-based survey was administered to 601 staff nurses working in 16 intensive care units at 5 acute care hospitals with sedation guidelines specifying delirium assessment in the Boston, Massachusetts area. RESULTS: Overall, 331 nurses (55%) responded. Only 3% ranked delirium as the most important condition to evaluate, compared with altered level of consciousness (44%), presence of pain (23%), or improper placement of an invasive device (21%). Delirium assessment was less common than sedation assessment (47% vs 98%, P < .001) and was more common among nurses who worked in medical intensive care units (55% vs 40%, P = .03) and at academic centers (53% vs 13%, P < .001). Preferred methods for assessing delirium included assessing ability to follow commands (78%), checking for agitation-related events (71%), the Confusion Assessment Method for the Intensive Care Unit (36%), the Intensive Care Delirium Screening Checklist (11%), and psychiatric consultation (9%). Barriers to assessment included intubation (38%), complexity of the tool for assessing delirium (34%), and sedation level (13%). CONCLUSIONS: Practice and perceptions of delirium assessment vary widely among critical care nurses despite the presence of institutional sedation guidelines that promote delirium assessment.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".