Spinal cord stimulation in patients with painful diabetic neuropathy: A multicentre randomized clinical trial
Bibliographic record
Abstract
Painful diabetic neuropathy (PDN) is a peripheral neuropathic pain condition that is often difficult to relieve. Spinal cord stimulation (SCS) is a proven effective therapy for various types of mixed neuropathic conditions, yet effectiveness of SCS treatment for PDN is not well established. To our knowledge, ours is the first multicentre randomized controlled trial investigating the effectiveness of SCS in patients with PDN. Sixty patients with PDN in the lower extremities refractory to conventional medical therapy were enrolled and followed for 6 months. They were randomized 2:1 to best conventional medical practice with (SCS group) or without (control group) additional SCS therapy, and both groups were assessed at regular intervals. At each follow-up visit, the EuroQoL 5D, the short form McGill Pain Questionnaire (SF-MPQ) and a visual analogue scale (VAS, ranging 0-100) to measure pain intensity were recorded. The average VAS score for pain intensity was 73 in the SCS group and 67 in the control group at baseline. After 6 months of treatment, the average VAS score was significantly reduced to 31 in the SCS group (P<.001) and remained 67 (P=.97) in the control group. The SF-MPQ and EuroQoL 5D questionnaires also showed that patients in the SCS group, unlike those in the control group, experienced reduced pain and improved health and quality of life after 6 months of treatment. In patients with refractory painful diabetic neuropathy, spinal cord stimulation therapy significantly reduced pain and improved quality of life.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".