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Record W2087675933 · doi:10.1097/wad.0b013e318193cbef

Verbal Repetition in People With Mild-to-Moderate Alzheimer Disease

2009· article· en· W2087675933 on OpenAlexaff
Cheryl Cook, Sherri Fay, Kenneth Rockwood

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2009
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRepetition (rhetorical device)DementiaDiseaseAudiologyPsychologyAlzheimer's diseaseMedicineRepeated measures designClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Verbal repetition is a common symptom and target for treatment in Alzheimer disease (AD), yet little is known of its manifestations in the daily lives of patients. Here we characterized the nature of verbal repetition and its correlates. METHODS: This is a qualitative, secondary analysis of video-recorded interviews with 130 community dwelling mild-to-moderate patients with Alzheimer disease and their carers, enrolled in the Video Imaging Synthesis of Treating Alzheimer's disease clinical trial. Narratives about verbal repetition were characterized using a qualitative framework analysis approach. RESULTS: Verbal repetition was reported in 100/130 patients, 57 of whom identified diminished repetition as a desired outcome of treatment. Most patients (76/100) repeated questions (usually about upcoming events); fewer (32/100) patients repeated statements/stories (usually about recent events). Most repetitions occurred within a 2-hour interval (65/100), and for 52/100 patients the problem was consistent (eg, occurred everyday). There were no differences for interval between repetitions by dementia severity, but most patients who repeated statements/stories were mild (27/32). CONCLUSIONS: Verbal repetition is a common problem, and seems especially to be provoked by upcoming events. More frequent repetitions (shorter intervals between each repetition) were associated with goal setting around this problem.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.290
Teacher spread0.276 · 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

Citations31
Published2009
Admission routes1
Has abstractyes

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