Identifying predictors of cognitive decline in long-term care: a scoping review
Bibliographic record
Abstract
Abstract Background Cognitive impairment can cause social, emotional, and financial burdens on individuals, caregivers, and healthcare providers. This is especially important in settings such as long-term care (LTC) homes which largely consist of vulnerable older adults. Thus, the objective of this study is to review and summarize current research examining risk factors of cognitive decline in older adults within LTC. Methods This scoping review includes primary observational research studies assessing within-person change in cognition over time in LTC or equivalent settings in high resource countries. A mean participant age of ≥ 65 years was required. Searches were conducted in Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and PyscInfo on June 27th, 2022 and included articles published during or after the year 2000. Title, abstract, and full-text screening was performed by two independent reviewers using Covidence. Specific predictors along with their associated relation with cognitive decline were extracted by a team of reviewers into a spreadsheet. Results Thirty-eight studies were included in this review. The mean sample size was 14 620. Eighty-seven unique predictors were examined in relation to cognitive decline. Dementia was the most studied predictor (examined by 9 of 38 studies), and the most conclusive, with eight of those studies identifying it as a risk factor for cognitive decline. Other predictors that were identified as risk factors included arterial stiffness (identified by 2 of 2 studies), physical frailty (2 of 2 studies), sub-syndromal delirium (2 of 2 studies), and undergoing the first wave of COVID-19 lockdowns (2 of 2 studies). ADL independence was the most conclusive protective factor (3 of 4 studies), followed by social engagement (2 of 3 studies). Many remaining predictors showed no association and/or conflicting results. Conclusions Dementia was the most common risk factor, while ADL independence was the most common protective factor associated with cognitive decline in LTC residents. This information can be used to stratify residents by risk severity and provide better personalized care for older adults through the targeted management of cognitive decline.
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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 | 0.016 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".