Effect of Age on Clinical Trial Outcome in Participants with Probable Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Age may affect treatment outcome in trials of mild probable Alzheimer's disease (AD). OBJECTIVE: We examined age as a moderator of outcome in an exploratory study of deep brain stimulation targeting the fornix (DBS-f) region in participants with AD. METHODS: Forty-two participants were implanted with DBS electrodes and randomized to double-blind DBS-f stimulation ("on") or sham DBS-f ("off") for 12 months. RESULTS: The intervention was safe and well tolerated. However, the selected clinical measures did not differentiate between the "on" and "off" groups in the intent to treat (ITT) population. There was a significant age by time interaction with the Alzheimer's Disease Assessment Scale; ADAS-cog-13 (p = 0.028). Six of the 12 enrolled participants < 65 years old (50%) markedly declined on the ADAS-cog-13 versus only 6.7%of the 30 participants≥65 years old regardless of treatment assignment (p = 0.005). While not significant, post-hoc analyses favored DBS-f "off" versus "on" over 12 months in the < 65 age group but favored DBS-f "on" versus "off" in the≥65 age group on all clinical metrics. On the integrated Alzheimer's Disease rating scale (iADRS), the effect size contrasting DBS-f "on" versus "off" changed from +0.2 (favoring "off") in the < 65 group to -0.52 (favoring "on") in the≥65 age group. CONCLUSION: The findings highlight issues with subject selection in clinical trials for AD. Faster disease progression in younger AD participants with different AD sub-types may influence the results. Biomarker confirmation and genotyping to differentiate AD subtypes is important for future clinical trials.
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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 | 0.068 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".