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Propentofylline Treatment for Alzheimer Disease and Vascular Dementia: An Economic Evaluation Based on Functional Abilities

2000· article· en· W2059543385 on OpenAlexaffabout
John Bachynsky, Peter McCracken, Doug Lier, Karine Alloul, Philip Jacobs

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2000
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsInstitute of Health EconomicsGlenrose Rehabilitation Hospital
Fundersnot available
KeywordsDementiaMedicineChristian ministryVascular dementiaEconomic evaluationGerontologyAlzheimer's diseaseDiseasePopulationPsychiatryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

A Canadian economic evaluation of propentofylline (a therapy shown to be effective for patients with mild to moderate Alzheimer disease and/or vascular dementia) versus standard care was conducted. Patients were categorized by functional abilities according to the Alberta Resident Classification System by translating measures that were originally captured through the Gottfries-Bråne-Steen scale. The Alberta Resident Classification System was then linked to a community dataset of home care costs for a population with dementia. Cost and cost-effectiveness analyses were performed from the perspective of the Ministry of Health, the caregiver, and society using an intent-to-treat analysis for propentofylline versus placebo. Results, limited to the 48-week clinical trial duration, indicated that propentofylline improved health outcomes of persons with dementia as statistically significant treatment effects were found. However, although an incremental cost for the propentofylline intervention was incurred from the Ministry of Health perspective, home care and, to a larger extent, caregiver costs were reduced. Savings in these areas may have partially offset annual treatment medication costs because a non-statistically significant cost difference was observed from a societal perspective.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0060.000

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 head0.041
GPT teacher head0.321
Teacher spread0.280 · 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.

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

Citations18
Published2000
Admission routes2
Has abstractyes

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