SPATIAL, TEMPORAL, AND VARIABILITY NORMS FROM THE GAITRITE SYSTEM PREDICT MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Mounting evidence indicates associations among temporal, spatial, and variability metrics of gait and clinical outcomes including fall risk, mild cognitive impairment (MCI), and movement disorders. Data from the Victoria Longitudinal Study (VLS) for select cohorts and retest waves were employed as a cross-sectional reference sample of older adults with intact cognitive performance and no history of falls. Participants (n=213) were 70 to 85 years of age (M=77.00, SD=4.22), with 152 women and 61 men. Regression-norming techniques were employed in the PREVENT Study, a multifactorial investigation of dementia, to identify participants with MCI. PREVENT participants (Controls=23, MCI=11) were 72 to 83 years of age (M=77.45, SD=4.23) with 20 women and 14 men. Select gait metrics were gathered from both samples using a 16-foot GAITRite computerized walkway. Participants walked across the mat at a self-determined normal pace a total of 8 times comprising 2 conditions: a walk-only condition (4 passes at a normal pace) and walking under cognitive load (4 passes counting backwards). The combination of velocity (under both conditions), single support time (walk-only) and Stride Time SD (walk-only) yielded a 90.6% MCI-classification accuracy (81.8% sensitivity; 95.2% specificity). Each SD increase in velocity under cognitive load was associated with a 25-fold decreased risk of MCI classification, while each SD increase in single support time was associated with a 17.75-fold increased risk. Findings provide strong preliminary evidence that regression-derived norms of specific GAITRite indicators can facilitate identification of MCI risk within an independent sample.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".