Guidelines for Gait Assessments in the Canadian Consortium on Neurodegeneration in Aging (CCNA)
Bibliographic record
Abstract
BACKGROUND: Motor and cognitive impairments are common among older adults and often co-exist, increasing their risk of dementia, falls, and fractures. Gait performance is an accepted indicator of global health and it has been proposed as a valid motor marker to detect older adults at risk of developing mobility and cognitive declines including future falls and incident dementia. Our goal was to provide a gait assessment protocol to be used for clinical and research purposes. METHODS: Based on a consensus that identified common evaluations to assess motor-cognitive interactions in community-dwelling older individuals, a protocol on how to evaluate gait in older adults for the Canadian Consortium on Neurodegeneration in Aging (CCNA) was developed. RESULTS: The CCNA gait assessment includes preferred and fast pace gait, and dual-task gait that comprises walking while performing three cognitively demanding tasks: counting backwards by ones, counting backwards by sevens, and naming animals. This gait protocol can be implemented using an electronic-walkway, as well as by using a regular stopwatch. The latter approach provides a simple manner to evaluate quantitative gait performance in clinics. CONCLUSIONS: Establishing a standardized gait assessment protocol will help to assess motor-cognitive interactions in aging and neurodegeneration, to compare results across studies, and to feasibly implement and translate gait testing in clinics for detecting impending cognitive and mobility decline.
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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 | 0.025 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".