The association between cognitive fluctuations and activities of daily living and quality of life among institutionalized patients with dementia
Bibliographic record
Abstract
Objective Cognitive fluctuations (CFs) occur commonly in dementia of all types. While it is generally accepted that CFs can affect the clinical rating of dementia severity and neuropsychological performance, little is known about their impact on patients' activities of daily living (ADLs) and quality of life (QOL). Our study aims to explore the impact of CFs on ADLs and QOL among institutionalized patients with dementia. Methods The present study examined the nature and frequency of CFs in 55 institutionalized dementia patients. We used the Dementia Cognitive Fluctuation Scale (DCFS) to assess the presence and severity of CFs. The Alzheimer's Disease Functional Assessment of Change Scale (ADFACS) was used to assess patients' ADLs, and the Quality of Life in Late Stage Dementia scale (QUALID) was used to assess QOL. Linear regression models were used to assess the relationships between CFs, ADLs, and QOL. Results The mean age of the patients was 90.41 years (SD = 2.84). Their mean Aggressive Behavior Scale score was 1.13 (SD = 1.59), mean Severe Impairment Battery total score was 86.65 (SD = 13.77), and mean DCFS score was 10.07 (SD = 3.04). The mean ADFACS‐ADL score was 10.88 (SD = 6.37), mean ADFACS‐IADL score was 16.61 (SD = 9.54), and mean QUALID total score was 18.25 (SD = 5.70). DCFS significantly predicted ADFACS‐ADL score (R2 = 0.39, β = 0.30, P = .011) although the relationship between ADFACS‐IADL score and DCFS score was not significant (R2 = 0.16, P = .111). DCFS significantly predicted QUALID score (R2 = 0.08, β = 0.29, P = .033). Conclusion More severe CFs in patients with dementia were significantly associated with impaired ability to engage in ADLs and poorer QOL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".