Serial Ottawa 3DY assessments to detect delirium in older emergency department community dwellers
Bibliographic record
Abstract
BACKGROUND: delirium is associated with increased morbidity and mortality among older emergency department (ED) patients. When using physician gestalt, delirium is missed in the majority of patients. The Ottawa 3DY (O3DY) has been validated to detect cognitive dysfunction among older ED patients. OBJECTIVES: to determine the sensitivity and specificity of serial O3DY assessments to detect delirium in older ED patients. DESIGN: a prospective observational multicenter cohort study. SETTING: four Quebec EDs. PARTICIPANTS: independent or semi-independent older patients (age ≥ 65 years) with an ED stay of at least 8 hours that required hospitalisation. MEASUREMENTS: eligible patients were evaluated using serial O3DY assessments at least 6 hours apart. The primary outcome was delirium after at least 8 hours in the ED. The reference standard for delirium assessment was the confusion assessment method (CAM). The sensitivity and specificity of the serial O3DY to detect delirium were calculated. RESULTS: we enrolled 301 patients (mean age 77 years, 49.5% male, 3.0% with a history of mild dementia). Thirty patients (10.0%) were CAM positive for delirium. Patients had a median of three O3DY assessments. Serial O3DY evaluations to detect delirium among patients with at least one abnormal O3DY had a sensitivity of 86.7% (95% confidence interval-CI 69.3-96.2%) and a specificity of 44.3% (95%; CI 38.3-50.4%). CONCLUSION: serial O3DY testing demonstrates good sensitivity as a screening tool to detect delirium among older adult patients with prolonged ED lengths of stay. Emergency physicians should consider the use of the serial O3DY over clinician gestalt to improve delirium detection.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".