Verbal Repetition in People With Mild-to-Moderate Alzheimer Disease
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".