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Record W2069362858 · doi:10.1177/089198870001300402

Review of Outcome Measurement Instruments in Alzheimer's Disease Drug Trials: Psychometric Properties of Functional and Quality of Life Scales

2000· review· en· W2069362858 on OpenAlexaff
Louise Demers, Mark Oremus, Anne Perrault, Nathalie Champoux, Christina Wolfson

Bibliographic record

VenueJournal of Geriatric Psychiatry and Neurology · 2000
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsJewish General Hospital
Fundersnot available
KeywordsActivities of daily livingQuality of life (healthcare)DementiaReliability (semiconductor)PsychologyScale (ratio)Rating scalePsychometricsAlzheimer's diseaseGerontologyClinical psychologyDiseasePsychiatryMedicineDevelopmental psychologyPsychotherapist

Abstract

fetched live from OpenAlex

The psychometric properties of functional and quality of life outcome measures that were used for the purpose of showing changes in antidementia drug trials for Alzheimer's disease are described and critiqued. The seven functional scales reviewed for reliability, validity, and responsiveness to change included the Geriatric Evaluation by Relative's Rating Instrument, the Physical Self-Maintenance Scale, the Instrumental Activities of Daily Living, the Blessed Dementia Scale, Part 1 and its revised version, the Interview for Deterioration in Daily Living with Dementia, the Unified Activities of Daily Living, and the Dependence Scale. The Progressive Deterioration Scale and Quality of Life Assessment were classified as quality of life scales. The majority of the scales were found to exhibit serious limitations, such as incomplete reliability and validity assessment for the intended uses. The most pervasive problem was a lack of data on responsiveness to change. It is recommended that further research be conducted to develop new tools or enhance existing measures for the assessment of both quality of life and functional ability.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.528
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.234
GPT teacher head0.409
Teacher spread0.175 · 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
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

Citations53
Published2000
Admission routes1
Has abstractyes

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