Routine Use of the Confusion Assessment Method for the Intensive Care Unit: A Multicenter Study
Bibliographic record
Abstract
RATIONALE: Delirium is often unrecognized in ICU patients and associated with poor outcome. Screening for ICU delirium is recommended by several medical organizations to improve early diagnosis and treatment. The Confusion Assessment Method for the ICU (CAM-ICU) has high sensitivity and specificity for delirium when administered by research nurses. However, test characteristics of the CAM-ICU as performed in routine practice are unclear. OBJECTIVES: To investigate the diagnostic value of the CAM-ICU in daily practice. METHODS: Teams of three delirium experts including psychiatrists, geriatricians, and neurologists visited 10 ICUs twice. Based on cognitive examination, inspection of medical files, and Diagnostic and Statistic Manual of Mental Disorders, 4th edition, Text Revision criteria for delirium, the expert teams classified patients as awake and not delirious, delirious, or comatose. This served as a gold standard to which the CAM-ICU as performed by the bedside ICU-nurses was compared. Assessors were unaware of each other's conclusions. MEASUREMENTS AND MAIN RESULTS: Fifteen delirium experts assessed 282 patients of whom 101 (36%) were comatose and excluded. In the remaining 181 (64%) patients, the CAM-ICU had a sensitivity of 47% (95% confidence interval [CI], 35%-58%); specificity of 98% (95% CI, 93%-100%); positive predictive value of 95% (95% CI, 80%-99%); and negative predictive value of 72% (95% CI, 64%-79%). The positive likelihood ratio was 24.7 (95% CI, 6.1-100) and the negative likelihood ratio was 0.5 (95% CI, 0.4-0.8). CONCLUSIONS: Specificity of the CAM-ICU as performed in routine practice seems to be high but sensitivity is low. This hampers early detection of delirium by the CAM-ICU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".