Prevalence and incidence of delirium in long‐term care
Bibliographic record
Abstract
OBJECTIVES: (1) To describe the prevalence and 6-month incidence of delirium in long-term care facility (LTCF) residents age 65 and over; (2) To describe differences in these measures by resident baseline characteristics. METHODS: A multisite, prospective observational study was conducted in seven LTCFs in the province of Quebec, Canada. Residents of age 65 and over were recruited into two cohorts: Cohort A with a Mini-Mental State Examination (MMSE) score of 10 or more and Cohort B with an MMSE score of less than 10. Baseline resident measurements were obtained from research resident assessments, nurse interviews, and chart review. Weekly resident assessments for up to 24 weeks included the Confusion Assessment Method (CAM). RESULTS: Two hundred seventy-nine residents were recruited and completed baseline delirium assessments: 204 in Cohort A and 75 in Cohort B. The prevalence of delirium at baseline was 3.4% in Cohort A and 33.3% in Cohort B. The incidence of delirium among those without delirium at baseline (per 100 person-weeks of follow-up) was 1.6 in Cohort A and 6.9 in Cohort B. In multivariate analyses, a diagnosis of dementia, moderate to severe cognitive impairment, and depressive symptoms at baseline were associated with a greater prevalence and incidence of delirium. CONCLUSION: Delirium is an important clinical problem in LTCF residents, particularly among those with moderate to severe cognitive impairment at baseline.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| 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".