Prevalence and detection of delirium in elderly emergency department patients.
Bibliographic record
Abstract
BACKGROUND: Delirium is a complex medical disorder associated with high morbidity and mortality among elderly patients. The goals of our study were to determine the prevalence of delirium in emergency department (ED) patients aged 65 years and over and to determine the sensitivity and specificity of a conventional clinical assessment by an ED physician for the detection of delirium in the same population. METHODS: All elderly patients presenting to the ED in a primary acute care, university-affiliated hospital who were triaged to the observation room on a stretcher because of the severity of their illness were screened for delirium by a research psychiatrist using the Mini-Mental State Examination and the Confusion Assessment Method. The diagnosis of "delirium" or an equivalent term by the ED physician was determined by 2 methods: completion of a mental status checklist by the ED physician and chart review. The prevalence of delirium and the sensitivity and specificity of the ED physician's clinical assessment were calculated with their 95% confidence intervals. The demographic and clinical characteristics of patients with detected delirium and those with undetected delirium were compared. RESULTS: A sample of 447 patients was screened. The prevalence of delirium was 9.6% (95% confidence interval 6.9%-12.4%). The sensitivity of the detection of delirium by the ED physician was 35.3% and the specificity, 98.5%. Most patients with delirium had neurologic or pulmonary diseases, and most patients with detected delirium had neurologic diseases. INTERPRETATION: Despite the relatively high prevalence of delirium in elderly ED patients, the sensitivity of a conventional clinical assessment for this condition is low. There is a need to improve the detection of delirium by ED physicians.
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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 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".