Evaluating pain and neurological function with high frequency 10 kHz spinal cord stimulation in the treatment of painful diabetic neuropathy: design of a multicentre, randomised controlled trial (PDN-Sensory)
Bibliographic record
Abstract
INTRODUCTION: Current pharmacological treatment options for painful diabetic neuropathy (PDN) often fail to provide adequate pain relief. However, in the recent SENZA-PDN study, high-frequency 10 kHz spinal cord stimulation (SCS) demonstrated significant long-term improvements in lower limb pain and health-related quality of life (HRQoL) in a PDN population. Furthermore, more than half of 10 kHz SCS recipients showed improved sensory function based on non-blinded clinical assessments in post hoc analysis. We report the design of the PDN-Sensory study, which aims to evaluate changes in pain and neurological function with 10 kHz SCS in the treatment of PDN. The study will include objective measures of neurological function, including the modified Toronto Clinical Neuropathy Score (mTCNS) and intraepidermal nerve fibre density (IENFD). METHODS AND ANALYSIS: This multicentre, prospective, randomised controlled trial will compare conventional medical management (CMM) with 10 kHz SCS+CMM in individuals with diabetes and chronic, intractable lower limb pain due to PDN. Participants will be randomised 1:1 to CMM alone or 10 kHz SCS+CMM, with optional crossover at 6 months. The primary outcome is the proportion of participants at 6 months achieving ≥50% pain relief from baseline. The key secondary endpoint is the proportion of participants at 6 months with a reduction in mTCNS of ≥3 points from baseline (excluding changes in foot pain). Additional endpoints at 6 and 12 months include changes from baseline in mTCNS, IENFD, 7-day averaged pain score, pain-related interference, HRQoL, sleep, psychological outcomes, functional status and metabolic parameters. ETHICS AND DISSEMINATION: The study protocol received central approval from the Western Institutional Review Board (IRB #20230954). Local IRB approval will be required before initiation of the study at each participating clinical site. The study complies with Good Clinical Practice guidelines (ISO 14155), the Declaration of Helsinki, and all applicable national, federal and local regulatory requirements. Dissemination plans include presentations at national and international conferences and publication in a peer-reviewed journal with open access. TRIAL REGISTRATION NUMBER: NCT05777317.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".