P3‐022: INDIVIDUALIZED SYMPTOM TRACKING WITH SYMPTOMGUIDE<sup>TM</sup> ALLOWS FOR CLINICALLY MEANINGFUL INTERPRETATION OF MMSE SCORE CHANGES IN A DEMENTIA DRUG TRIAL
Bibliographic record
Abstract
A positive treatment response reflects a significant improvement in outcome measures in treated patients compared to placebo. Although such differences may be statistically significant they may not represent clinically meaningful change to patients or their caregivers. We compared change in SymptomGuide™-dementia (SG-D; clinically meaningful symptoms set as treatment goals by caregivers) to change in Mini-Mental State Examination (MMSE) scores. This secondary analysis used data from VASPECT, a six-month open-label trial of donepezil. Subjects with SG-D data available (N=128/148) were 75.4±9.2 years old (52.3% women) with mild-moderate vascular dementia (VaD) or mixed Alzheimer/vascular dementia (AD/VaD). The SG-D is a symptom-tracking system with a menu of 32 symptoms (8-12 descriptors/symptom) with options to add unique symptoms. Caregivers identified symptoms important to them and rated change on a 7-point scale from very much improved (+3) to very much worse (-3) at three and six months. Efficacy was measured as change in MMSE at six months. The 100 subjects who completed the study with SG-D data available were analyzed. SG-D symptoms were categorized into five domains: behaviour, cognition, daily function, executive function, and physical manifestations. On average, caregivers set 8.1 symptoms. The most common were executive function (268/811, 33%) and cognition symptoms (252/811, 31%). Overall symptom change correlated with MMSE change (r=0.35, p<0.001) at six months. When subjects were stratified by a 2-point MMSE change, net improvement was seen across all domains (mean Behaviour goal rating=0.77, p=0.005; Cognition: 0.68, p<0.001; Daily Function: 0.32, p=0.008, Executive Function: 0.91, p<0.001; Physical Manifestations: 0.75, p<0.001). Subjects with MMSE decline also declined only in daily function symptoms (−0.94, p=0.002). “Social Interaction/Withdrawal” symptoms most often responded to a 2-point improvement on MMSE (OR=13.0, 95% CI 1.70−99.38, p=0.013), followed by “Disorientation to Time” (11.3, 1.61−78.57, p=0.015), and “Interest/Initiative” (9.0, 1.42−57.12, p=0.020). A 2-point change in MMSE correlated with changes in individualized SG-D symptoms in people with VaD or AD/VaD. Outcome measures, such as the SG-D, that are inherently clinically meaningful to patients and their caregivers, can help translate from clinical trials into everyday dementia practice.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".