Rates of Cognitive Change in Alzheimer Disease
Bibliographic record
Abstract
Treatment success in Alzheimer disease (AD) trials is generally based on benefits over placebo-treated controls. Consequently, variation in rates of decline among placebo-treated patients could impact outcomes from AD trials. In the present analyses, individual patient data [baseline Mini-Mental State Examination (MMSE): 10 to 26] were pooled from randomized, placebo-controlled studies of donepezil for AD conducted during the 1990s, and grouped by initiation year-group 1: 1990 to 1994; group 2: 1996 to 1999. Changes in MMSE and Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) were compared between groups 1 and 2 for placebo, and then between donepezil and placebo. Data were available from 3403 patients in 13 trials. Group 2 (post-1995) included patients with lower baseline MMSE scores, older patients, fewer males, more comorbidity, and more concomitant medications. MMSE decline by week 24 was significantly greater among group 1 (pre-1995) placebo patients versus group 2; a similar trend was observed with the ADAS-cog. Nevertheless, donepezil-mediated treatment effects were consistent over the decade of enrollment. These analyses suggest that patients are showing slower rates of cognitive decline in more recent trials compared with older trials, although having more comorbidities. This finding may have important potential implications for future clinical trial design.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".