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Treatment Effect Size of Memantine Therapy in Alzheimer Disease and Vascular Dementia

2006· review· en· W2014792393 on OpenAlexaff
Matthew Smith, Jennie Wells, Michael Borrie

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2006
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease and Spinal Disorders
Canadian institutionsWestern UniversityLawson Health Research Institute
Fundersnot available
KeywordsMemantineDementiaVascular dementiaMedicineClinical trialAlzheimer's diseaseDonepezilMeta-analysisPsycINFOCochrane LibraryInternal medicineDiseasePsychiatryMEDLINE

Abstract

fetched live from OpenAlex

The purpose of this paper is to assess the clinical relevance of the significant results reported in clinical trials of memantine therapy for Alzheimer disease (AD) and vascular dementia. We sought to understand what clinically detectable changes would be evident to the patient with dementia, their caregivers, and the treating physician during a trial of memantine therapy. A literature search was performed on MedLine, PsycInfo, and the Cochrane database. The statistically significant findings from the memantine literature were reviewed using treatment effect size as a measure of clinical meaningfulness. There have been 3 phase 2 studies of memantine in dementia, and 6 phase 3 studies published as of August 1, 2005. Of the 6 published phase 3 trials of memantine in dementia; 2 were in mild-moderate vascular dementia, 1 in mild-moderate AD, 2 in moderate-severe AD, and 1 in severe dementia (both AD and vascular dementia). The most convincing evidence of a clinically meaningful treatment effect of memantine therapy is in the moderate-severe AD patient population. Cognition, as measured by the Severe Impairment Battery, had an effect size of 0.32 and 0.49 in the 2 published trials, indicating a small-to-medium effect of the medication. Global, functional, and behavioral scales also showed a small-to-medium response to memantine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.700
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.316
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations30
Published2006
Admission routes1
Has abstractyes

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