O2‐01‐07: Good days and bad days in dementia: Results of an online survey
Bibliographic record
Abstract
Caregivers report that people with dementia commonly exhibit notable day to day fluctuation in cognition, functioning and behaviour. Even so, this phenomenon of “good days and bad days” has received comparatively little study. Here, we report the results of an online survey to better understand daily symptom variation (DSV) in people with dementia. Respondents were recruited from the DementiaGuide website (www.dementiaguide.com) that employs the SymptomGuide™ as an online symptom tracking tool. Visitors were offered free use of SymptomGuide™ in exchange for completing a survey about DSV. The recruitment ran from December, 2007 to October, 2009. Of 991 SymptomGuide™ users during the recruitment period, 267 completed the survey, providing 145 profilees (55 men and 90 women) with dementia. The mean age of the people with dementia was 77 years. Two thirds of the profilees with dementia reported DSV. Respondents reported that 46% of the profilees (with both dementia and DSV) have 3-4 good or bad days a week, (range 1-7 days). In general, women exhibited more variability than did men. Of 97 profilees with DSV who could predict whether the day would be a “good day” or a “bad day”, 82% could do so before noon (including 43% shortly after awakening). Of the top 10 symptoms profiled by the respondents, 8 overlapped with the top 10 symptoms profiled by all 991 SymptomGuide™ users. The most common symptoms reported for subjects with DSV were irritability, concentration, memory, conversation, communication, and contentedness and what respondents characterized as overall “sharpness”. Daily symptom variability appears to be important and recognizable in the lived experience of people with dementia. How it impacts on test performance in clinical settings is not clear, but the reports of which symptoms most often show variability suggest that this might be important.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".