Delirium Assessment Tools for Use in Critically Ill Adults: A Psychometric Analysis and Systematic Review
Bibliographic record
Abstract
BACKGROUND: Delirium is highly prevalent in critically ill patients. Its detection with valid tools is crucial. OBJECTIVE: To analyze the development and psychometric properties of delirium assessment tools for critically ill adults. METHODS: Databases were searched to identify relevant studies. Inclusion criteria were English language, publication before January 2015, 30 or more patients, and patient population of critically ill adults (>18 years old). Search terms were delirium, scales, critically ill patients, adult, validity, and reliability. Thirty-six manuscripts were identified, encompassing 5 delirium assessment tools (Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), Cognitive Test for Delirium, Delirium Detection Score, Intensive Care Delirium Screening Checklist (ICDSC), and Nursing Delirium Screening Scale). Two independent reviewers analyzed the psychometric properties of these tools by using a standardized scoring system (range, 0-20) to assess the tool development process, reliability, validity, feasibility, and implementation of each tool. RESULTS: Psychometric properties were very good for the CAM-ICU (19.6) and the ICDSC (19.2), moderate for the Nursing Delirium Screening Scale (13.6), low for the Delirium Detection Score (11.2), and very low for the Cognitive Test for Delirium (8.2). CONCLUSIONS: The results indicate that the CAM-ICU and the ICDSC are the most valid and reliable delirium assessment tools for critically ill adults. Additional studies are needed to further validate these tools in critically ill patients with neurological disorders and those at various levels of sedation or consciousness.
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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 | 0.020 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".