The clinical meaningfulness of ADAS-Cog changes in Alzheimer's disease patients treated with donepezil in an open-label trial
Bibliographic record
Abstract
BACKGROUND: In 6-month anti-dementia drug trials, a 4-point change in the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) is held to be clinically important. We examined how this change compared with measures of clinical meaningfulness. METHODS: This is a secondary analysis of a 12 month open-label study of 100 patients (71 women) diagnosed with mild to moderate AD treated with 5-10 mg of donepezil daily. We studied the observed case, 6-month change from baseline on the ADAS-Cog, the Clinician's Interview Based Impression of Change-Plus Caregiver Input (CIBIC-Plus), patient-Goal Attainment Scaling (PGAS) and clinician-GAS (CGAS). RESULTS: At 6 months, donepezil-treated patients (n = 95) were more likely to show no change (+/- 3 points) on the ADAS-Cog (56%) than to improve (20%) or decline (24%) by 4-points. ADAS-Cog change scores were little correlated with other measures: from -0.09 for PGAS to 0.27 for the CIBIC-Plus. While patients who improved on the ADAS-Cog were less likely to decline on the clinical measures (26%), 43% of patients who declined on the ADAS-Cog improved on at least two of the clinical measures. CONCLUSION: The ADAS-Cog did not capture all clinically important effects. In general, ADAS-Cog improvement indicates clinical improvement, whereas many people with ADAS-Cog decline do not show clinical decline. The open-label design of this study does not allow us to know whether this is a treatment effect, which requires further investigation.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".