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 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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".