Symptoms of delirium predict incident delirium in older long-term care residents
Bibliographic record
Abstract
BACKGROUND: Detection of long-term care (LTC) residents at risk of delirium may lead to prevention of this disorder. The primary objective of this study was to determine if the presence of one or more Confusion Assessment Method (CAM) core symptoms of delirium at baseline assessment predicts incident delirium. Secondary objectives were to determine if the number or the type of symptoms predict incident delirium. METHODS: The study was a secondary analysis of data collected for a prospective study of delirium among older residents of seven LTC facilities in Montreal and Quebec City, Canada. The Mini-Mental State Exam (MMSE), CAM, Delirium Index (DI), Hierarchic Dementia Scale, Barthel Index, and Cornell Scale for Depression were completed at baseline. The MMSE, CAM, and DI were repeated weekly for six months. Multivariate Cox regression models were used to determine if baseline symptoms predict incident delirium. RESULTS: Of 273 residents, 40 (14.7%) developed incident delirium. Mean (SD) time to onset of delirium was 10.8 (7.4) weeks. When one or more CAM core symptoms were present at baseline, the Hazard Ratio (HR) for incident delirium was 3.5 (95% CI = 1.4, 8.9). The HRs for number of symptoms present ranged from 2.9 (95% CI = 1.0, 8.3) for one symptom to 3.8 (95% CI = 1.3, 11.0) for three symptoms. The HR for one type of symptom, fluctuation, was 2.2 (95% CI = 1.2, 4.2). CONCLUSION: The presence of CAM core symptoms at baseline assessment predicts incident delirium in older LTC residents. These findings have potentially important implications for clinical practice and research in LTC settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".