Treatment effects of Memantine on language in moderate to severe Alzheimer's disease patients
Bibliographic record
Abstract
Background Language impairment is one of the most troublesome manifestations of Alzheimer's disease (AD). The objective of this post hoc analysis was to assess the treatment effects of Memantine on language in patients with moderate to severe AD, using the recently developed Severe Impairment Battery‐Language (SIB‐L) scale. Methods From a combined database including four Memantine clinical trials in moderate‐to‐severe AD, we analyzed 801 patients with SIB‐L scores of <38 and Mini‐Mental State Examination scores of <15. Patients were treated with either 20 mg Memantine per day or placebo. Mean changes in SIB‐L scores from baseline were calculated. For responder analyses, a change in SIB‐L score greater than or equal to the SIB‐L measurement error of 3.7 points was considered a clinically relevant response. Results The mean change from baseline in SIB‐L score at week 12 and weeks 24/28 (study end) significantly favored Memantine over placebo treatment (P < .0001 and P = .0182, respectively). Overall, more Memantine‐treated patients than placebo‐treated patients benefited from treatment. The effect was especially pronounced in patients with substantial language impairment on the SIB‐L (baseline score, ≤20). At weeks 24/28, significantly more Memantine‐treated patients experienced a clinically relevant improvement (25.4% vs. 10.8%, P = .0414), and significantly fewer patients experienced clinically relevant worsening (32.8% vs. 60.0%, P = .0029). Conclusions Memantine treatment of AD patients results in significant benefits for language function. Our results suggest that it is worth considering this therapeutic option, even for AD patients with marked language impairment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".