GAIT UNSTEADINESS AS AN INDICATOR OF COGNITIVE STATUS IN INDIVIDUALS WITH PERIPHERAL NEUROPATHY
Bibliographic record
Abstract
Abstract Neuropathic individuals are at risk of falls, however potential impact of cognitive impairment in neuropathic individuals is not well-understood. Since cognitive impairment is considered an independent risk factor for falls, knowing its potential, additional impact may help better understand underlying mechanism of risk of falls in neuropathic individuals. We aimed to investigate stride-to-stride variability in neuropathic individuals with cognitive impairment (NP-Cog-Impaired) and without cognitive impairment (NP-Cog-Intact) during normal and dual-task walking. Neuropathic symptoms and cognitive status was measured using maximum vibration perception threshold (VPTmax) in the feet and the Montreal Cognitive Assessment (MoCA), respectively. We analyzed data from 19 NP-Cog-Impaired (8 men; 68.5±9.1 years; 29.0±6.2 kg/m2; VPTmax=27.2±12.1 volts; MoCA=19.6±2.4) and 25 NP-Cog-Intact (15 men; 66.5±9.1 years; 31.3±5.9 kg/m2; VPTmax=26.3±12.7 volts; MoCA=25.6±1.6). We collected movement data using five inertial sensors (LEGSysTM, BioSensics LLC, Watertown, MA) attached on the shanks, thighs and lower back. We used previously validated algorithm to calculate coefficient of variations (CV) of stride velocity and stride length. CV of stride velocity and stride length were significantly greater for the NP-Cog-Impaired group (11.07±5.22% and 7.31±3.20%, respectively) than for the NP-Cog-Intact group (7.31±3.20% and 4.81±2.80%, respectively) for dual-task walking but not for normal walking. Between normal and dual-task walking, CV of stride velocity and stride length increased 43.2% (significantly) and 46.4% (marginally), respectively, from normal walking to dual-task walking for the NP-Cog-Impaired group but not for the NP-Cog-Intact group. Results suggest that cognitive impairment may be an additional risk factor of falls in neuropathic individuals.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".