P1‐166: Assessing treatment responsiveness of anti‐dementia drugs with the SymptomGuide™
Bibliographic record
Abstract
Evaluation of individual patients’ treatment responses can be complicated. This reflects both the high dimensionality of dementia (e.g. impairments in cognition, function, behaviour) and that patients show different response profiles. Individualized approaches such as Goal Attainment Scaling responsively capture patient/caregiver preferences, but can be infeasible for routine use. The SymptomGuide (SG™) was developed to allow feasible, routine individualization. Patients/caregivers can select from 10-12 descriptions of 70 symptoms, and track changes in their profiles. SG™ scores reflect change from baseline, summarizing the number of goals set, their weights and the degree of change. Here, we compare the responsiveness (sensitivity to change) of SG™ scores to other clinical measures. SG™ profiles were generated for Capital Health Memory Clinic patients in Halifax, Canada. Responsiveness of the SG™, Mini-Mental State Examination (MMSE), Physical Self-Maintenance Scale (PSMS), Instrumental Activities of Daily Living (IADL) scale and Global Deterioration Scale (GDS) were compared using standardized response means (SRM) and relative efficiency (RE) scores. From 2007-2010, profiles were recorded for 335 patients treated by one clinic physician (KR). No patient declined participation. Their mean age (SD) was 77.6 (11.2) years; 188 (56.1%) were women; most (96.4%) lived with their spouse, family, or friend. Half had more than one clinic visit, allowing responsiveness to be calculated. The 10 most common symptoms were from the Executive Function and Cognition domains. Recent Memory and Verbal Repetition were each targeted in 69% or 58% of patients respectively. The SG™, but not MMSE showed clinically detectable change (SG™: Cohen's d = 0.34, MMSE: Cohen's d = 0.11) respectively up to 8 months. By 14 months only the SG™ showed significant change (Cohen's d = 0.44). By 24 months, statistically significant, clinically detectable, mild deterioration was seen on average. The 8 month REs were 8.22 for the SG™; 0.64 for MMSE; 0.02 for IADL; 0.99 for PSMS. Most SG™ worsening was seen with cognitive and behavioural symptoms. The SG™ and MMSE were the most responsive measures used in routine clinical evaluation. The MMSE reflects cognitive change. The SG™ targets the most troublesome symptoms experienced by each patient, providing a more clinically recognizable picture of how patients’ daily lives have changed.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".