Investigating the role of neuropathic pain relief in decreasing gait variability in diabetes mellitus patients with neuropathic pain: a randomized, double-blind crossover trial
Bibliographic record
Abstract
BACKGROUND: Subjects with diabetes mellitus (DM) develop gait dysfunction contributing to falls, reluctance to perform activities and injuries. Neuropathic pain (NeP) related to diabetic peripheral neuropathy (DPN) is associated with increased gait variability that may contribute to gait dysfunction. We used a portable device (GaitMeter™) and related gait and balance measures to measure gait parameters in painful DPN (PDPN) subjects prior to and during analgesia. Our hypothesis was that PDPN subjects would have decreased gait step variability when receiving pharmacological relief of NeP. METHODS: DPN subjects with at least moderate NeP were assessed in a randomized, double-blind crossover study of pregabalin versus placebo. The outcome measure was variability in step length and step velocity. Testing for Timed Get-Up-and-Go Test, Tinetti Mobility Scales, Sway Testing, a Physiological Profile Approach, and fall-related surveys were also performed. DPN severity was quantified using the Utah Early Neuropathy Score. RESULTS: PDPN subjects developed increased, rather than decreased, step length and step velocity variability during pregabalin treatment. There were no significant differences between cohorts for other physiological gait and balance testing. Non-significant NeP relief occurred in the pregabalin phase of study as compared with placebo. There was a negative relationship for step length with pain severity. CONCLUSION: Analgesia did not decrease gait variability in PDPN patients, and in fact, increased gait variability was seen during pregabalin treatment. Other important relationships of gait dysfunction with PDPN should be sought.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".