Prevalence, management and outcomes of unrecognized delirium in a National Sample of 1,493 older emergency department patients: how many were sent home and what happened to them?
Bibliographic record
Abstract
BACKGROUND: Retrospective studies estimate Emergency Department (ED) delirium recognition at <20%; few prospective studies have assessed delirium recognition and outcomes for patients with unrecognized delirium. OBJECTIVES: To prospectively measure delirium recognition by ED nurses and physicians, document their confidence in diagnosis and disposition, actual dispositions, and patient outcomes. METHODS: Prospective observational study of people ≥65 years. We assessed delirium using the Confusion Assessment Method, then asked ED staff if the patient had delirium, confidence in their assessment, if the patient could be discharged, and contacted patients 1 week postdischarge. We report proportions and 95% confidence intervals (Cls). RESULTS: We enrolled 1,493 participants; mean age was 77.9 years; 49.2% were female, 79 (5.3%, 95% CI 4.2-6.5%) had delirium. ED nurses missed delirium in 43/78 cases (55.1%, 95% CI 43.4-66.4%). Nurses considered 12/43 (27.9%) patients with unrecognized delirium safe to discharge. Median confidence in their delirium diagnosis for patients with unrecognized delirium was 7.0/10. Physicians missed delirium in 10/20 (50.0%, 95% CI 27.2-72.8) cases and considered 2/10 (20.0%) safe to discharge. Median confidence in their delirium diagnosis for patients with unrecognized delirium was 8.0/10. Fifteen patients with unrecognized delirium were sent home: 6.7% died at 1 week follow-up vs. none in those with recognized delirium and 1.1% in the rest of the cohort. CONCLUSION: Delirium recognition by nurses and physicians was sub-optimal at ~50% and may be associated with increased mortality. Research should explore root causes of unrecognized delirium, and novel strategies to systematically improve delirium recognition and patient outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".