The Effect of Multiple Assessments on Delirium Detection: a Pilot Study*
Bibliographic record
Abstract
BACKGROUND: Delirium is characterized by fluctuating attention or arousal, with high prevalence in the orthopaedic ward. Our aim was to: 1) establish the prevalence of delirium on an orthopaedic ward, and 2) compare delirium prevalence using a single geriatrician assessment vs. multiple 3D-CAM (3-Minute Diagnostic Interview for Confusion Assessment Method) assessments during the day. We hypothesized that multiple assessments would increase the detection rate due to the fluctuating nature of delirium. METHODS: Comparative study conducted at an academic hospital in Hamilton, Ontario. Participants included patients 65 years and older admitted to the orthopaedic ward (n=55). After a geriatrician made the first assessment of delirium by 3D-CAM on each patient, teams with specialized geriatrics training re-assessed participants up to four times. Delirium rates based on first assessment were compared to cumulative end-of-day rates to determine if detection increased with multiple assessments. RESULTS: The prevalence of delirium was 30.9% (17 participants) using multiple assessments. Of these cases, 13 (76.4%) were detected in the initial geriatrician assessment. In patients with hip fractures, 70.6% (12 of 17) were identified as delirious by multiple assessments. CONCLUSION: As symptoms fluctuate, multiple daily CAM assessments may increase the identification of delirium in orthopaedic inpatients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".