P3‐407: Good days and bad days in Alzheimer's disease: A pilot study
Bibliographic record
Abstract
Patients with dementia and their caregivers commonly describe significant fluctuation in symptom expression (good or bad days). Such changes are well described for some dementias (e.g., Lewy Body dementia), but less so for Alzheimer's disease (AD). We undertook a pilot study to determine how to best elicit descriptions of this phenomenon in patients with AD. Results will be used to inform a more in-depth inquiry. Mixed-methods pilot study of structured and semi-structured questions (based on relevant literature and previous retrospective analyses) designed to elicit descriptions of symptom fluctuation, precipitating factors, and duration of good/bad episodes from caregivers of patients with mild-moderate AD attending the Halifax Infirmary Memory Clinic (Canada). Interviews were conducted in-clinic at baseline and by telephone at 2-weeks and 2-months. Data were analyzed using descriptive statistics (survey responses) and framework analysis (transcribed interviews). Participants were 25 caregivers (mean 61 yrs ±12.6; 68% female; 80% spouse) of patients with mild-moderate AD (mean 75 yrs ±11.3; 56% female; 72% mild; FAST = 4.4 ±0.6; IADL = 19 ±6.1; PSMS = 8.6±3.5). Survey data indicated that on better days patients ‘remember more’ (87% at BL) and were ‘more content’ (78% at BL). On worse days patients were ‘less sharp’ (77% at BL) and ‘less able to concentrate’ (68% at BL). During interviews, participants described changes in mood, communication and ability to complete daily tasks as common indicators of good/bad days. Survey and interview data agreed that periods of fluctuation characterized as good commonly last longer than those characterized as bad. Interview responses highlighted the importance of environmental factors and found that symptom patterns did not maintain over time. Many caregivers reported they could predict whether a day would be good or bad, usually by noon, but often described routine daily fluctuations during interviews. Results suggest a less structured, more in-depth approach to questioning may elicit more robust descriptions of good days/bad days. Caregivers had difficulty identifying the marked changes we aimed to characterize, potentially due to limited symptom fluctuation in mild AD. Selecting caregivers of moderate AD patients and inquiring about what is considered “normal” before examining good/bad days may improve our ability to describe this phenomenon.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".