Postural sway serves as a predictive biomarker in balance and gait assessments for diabetic peripheral neuropathy screening: a community-based study
Bibliographic record
Abstract
BACKGROUND: Traditional screening methods for diabetic peripheral neuropathy (DPN) can be time-consuming in community settings. Balance and gait impairments are common in individuals with DPN, but these functional impairments are often not detectable with standard neurological examinations. This study aimed to examine whether quantitative balance and gait assessment could serve as a viable alternative screening tool for DPN. METHODS: All participants were recruited from a community-based daycare center and underwent peripheral nerve function assessments, including the Toronto Clinical Neuropathy Score (TCNS), sural nerve conduction studies (amplitude and velocity) for large fiber function, and Sudoscan testing for small fiber function. Subsequently, participants underwent balance and gait assessments, including static postural sway measurements and gait analysis of spatiotemporal parameters and joint range of motion (ROM) assessment during walking. RESULTS: Of the 146 participants, 35 had diabetes, including 22 with DPN, while 111 were healthy controls. Participants with DPN demonstrate increased postural sway velocity and total path length, along with reduced gait speed, shorter stride length, and decreased range of motion in hip flexion and extension. The logistic regression analysis identified diabetes duration and postural sway velocity as the only significant predictors of DPN presence. Postural sway velocity demonstrated strong correlations with elevated TCNS, reduced sural sensory nerve action potential and sensory nerve conduction velocity, and lower Sudoscan values in hands and feet. Additionally, receiver operating characteristic analysis yielded a sensitivity of 68.2%, specificity of 85.5%, and an area under the curve of 0.76, with a cut-off value of 0.98 cm/s. CONCLUSIONS: Balance and gait impairments are prevalent among participants with DPN. This study supports the integration of balance and gait assessments into community-based screening protocols to facilitate early identification and intervention. Postural sway velocity emerged as a practical early biomarker for the screening of DPN.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".