Environmental Factors Predict the Severity of Delirium Symptoms in Long‐Term Care Residents with and without Delirium
Bibliographic record
Abstract
OBJECTIVES: To identify potentially modifiable environmental factors (including number of medications) associated with changes over time in the severity of delirium symptoms and to explore the interactions between these factors and resident baseline vulnerability. DESIGN: Prospective, observational cohort study. SETTING: Seven long-term care (LTC) facilities. PARTICIPANTS: Two hundred seventy-two LTC residents aged 65 and older with and without delirium. MEASUREMENTS: Weekly assessments (for up to 6 months) of the severity of delirium symptoms using the Delirium Index (DI), environmental risk factors, and number of medications. Baseline vulnerability measures included a diagnosis of dementia and a delirium risk score. Associations between environmental factors, medications, and weekly changes in DI were analyzed using a general linear model with correlated errors. RESULTS: Six potentially modifiable environmental factors predicted weekly changes in DI (absence of reading glasses, aids to orientation, family member, and glass of water and presence of bed rails and other restraints) as did the prescription of two or more new medications. Residents with dementia appeared to be more sensitive to the effects of these factors. CONCLUSION: Six environmental factors and prescription of two or more new medications predicted changes in the severity of delirium symptoms. These risk factors are potentially modifiable through improved LTC clinical practices.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".