MétaCan
Menu
Back to cohort
Record W2039855223 · doi:10.2147/ndt.s5021

Critical appraisal of the long-term impact of memantine in treatment of moderate to severe Alzheimer’s disease

2009· article· en· W2039855223 on OpenAlexaffabout
Ging‐Yuek Robin Hsiung, Umamon Puangthong

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2009
Typearticle
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMemantineDiseaseApraxiaDementiaRivastigmineCognitionCognitive declineCaregiver burdenAgnosiaPsychiatryDonepezilAphasiaAudiologyPathology

Abstract

fetched live from OpenAlex

Critical appraisal of the long-term impact of memantine in treatment of moderate to severe Alzheimer’s disease Umamon Puangthong, Ging-Yuek Robin HsiungDivision of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, CanadaAbstract: Alzheimer’s disease is the most common cause of dementia in older adults. The clinical features include progressive memory decline as well as cognitive deficits with executive dysfunction, language, visual perceptual difficulties, apraxia and agnosia. During the moderate to severe stage of the disease, there is a major decline in memory and function, while neuropsychiatric disturbances often emerge and patients become difficult to manage. These distressing symptoms increase caregiver burden and add to the direct costs of care of the patients. Any improvements in patient function and behavioral symptoms can reduce caregiver burden. Memantine has been available for a number of years in Europe and in North America. In this article, we examine the pharmacological rationale for its use, and the current clinical evidence for its efficacy and long-term effectiveness in the management of cognitive and behavioral symptoms in moderate to severe stages of Alzheimer’s disease.Keywords: memantine, Alzheimer’s disease, dementia

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.037
GPT teacher head0.368
Teacher spread0.331 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueNeuropsychiatric Disease and TreatmentSame topicCholinesterase and Neurodegenerative DiseasesFrench-language works237,207