P1‐193: Good days and bad days in dementia: A qualitative analysis of variability in symptom expression
Bibliographic record
Abstract
People with dementia and their carers often refer to “good days”, when their condition seems better than usual, and “bad days” when it seems worse. Despite the importance of such variability in the lived experience of dementia, there has been little study of this outside Lewy Body dementia, where cognitive fluctuation is a core feature. Here, we anlaysed what characteristics patients/caregivers describe as “good days/bad days”. We reviewed charts of dementia patients by one of us (KR) between January 2007 and December 2008, looking for descriptions of good and bad days. Clinic note texts were transcribed and analysed using a qualitative framework analysis approach. Descriptive statistics were used to describe the sample. We identified 23 community-dwelling people (age 69-91;13 men) with AD (n=15, 65%) and mixed dementia (n=8) who were described as having good and bad days. Most (n=17) lived with their carers (primarily spouses=13). Patients tended to be newly diagnosed (M=0.77 years, sd=1.15), with mild dementia (16 with MMSE>19, mean=20.3, sd=6.2;11 with FAST≤4). Approximately half of the patients (n=12) were already taking with cholinesterase inhibitors. Descriptions of good and bad days fell into three general categories: the single symptom set in which good and bad days were attributed to changes in the same set of symptoms (e.g., less/more verbal repetition), the different symptom set (e.g., more interest on a good day, more agitation on a bad day), and the single extreme set in which only sentinel events that made a day good or bad days were acknowledged. Good days were associated with increased initiation, better mood, improved concentration and improved ability to perform IADLs. Bad days were associated with worse verbal repetition, anger/irritability, forgetfulness, delusions and worsening mood. Several patterns of good days and bad days are recognizable and important to patients and caregivers. Fluctuation in executive function appears prominent. Even so, what makes a “good” day good is not simply more (or less) of what makes a “bad” day bad. Given the importance of caregiver reports in dementia drug trials, a better understanding of this clinically relevant phenomenon is needed.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".