Key factors for the assessment of mobility in advanced dementia: A consensus approach
Bibliographic record
Abstract
INTRODUCTION: Loss of mobility is common in advanced dementia and has important negative consequences related to fall risk, loss of independence, and lack of participation in meaningful activities. The causes of decline are multifactorial, including disease-specific changes in motor function, behavior, and cognition. To optimize clinical management of mobility, there is a need to better characterize capacity for safe and independent mobility. This study aimed to identify key factors that impact on mobility in dementia. METHODS: Expert input was gathered using a modified Delphi consensus approach. The primary criterion for participation was specialist knowledge in mobility or dementia, either as a clinician or a researcher. Participants rated elements of mobility for importance and feasibility of assessment in advanced dementia and prioritized items for inclusion in a mobility staging tool. Descriptive statistics and qualitative content analysis were used to summarize responses. RESULTS: Thirty-six experts completed the first survey with an 80% retention rate over three rounds. One-third of 61 items reached consensus for being both important and feasible to assess, representing five categories of elements. Items reaching agreement for a staging tool included walking, parkinsonism, gait, impulsivity, fall history, agitation, transfers, and posture control. DISCUSSION: This study highlights the need for a multidimensional, dementia-specific approach to mobility assessment. Results have implications for development of assessment methods and management guidelines to support the clinical care of mobility impairment in people with dementia.
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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 | 0.191 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".