Spinal Cord Stimulation for the Treatment of Chronic Pain Reduces Opioid Use and Results in Superior Clinical Outcomes When Used Without Opioids
Bibliographic record
Abstract
BACKGROUND: Chronic pain causes a significant burden to the US health care system, is difficult to treat, and remains a significant contributor to increased opioid use in the United States. Spinal cord stimulation (SCS) has been FDA approved for the treatment of chronic pain. OBJECTIVE: To evaluate the hypothesis that SCS reduces opioid use, and alone maintains clinical outcome measures of pain and psychosocial determinants of health. METHODS: In this prospective cohort study, we evaluated 86 patients undergoing SCS surgery for the treatment of chronic pain between September 2012 and August 2015. Preoperatively and postoperatively, patients completed the Numerical Rating Scale (NRS), McGill Pain Questionnaire (MPQ), Pain Catastrophizing Scale (PCS), Oswestry Disability Index (ODI), and Beck's Depression Inventory (BDI). VAS scores were retrospectively analyzed. RESULTS: Fifty-three patients used opioids before SCS implantation. The 33 nonusers had lower mean VAS, NRS, and ODI scores than both opioid groups at 1 yr and improved significantly at 1 yr on the VAS (P < .001), NRS (P < .001), MPQ (P = .002), PCS (P < .001), BDI (P = .04), and ODI (P = .002). After surgery, 41.5% remained opioids and 58.5% reduced/eliminated use. Discontinued (n = 29) or reduced (n = 2) use resulted in VAS, NRS, total MPQ, and ODI score reduction (P < .001, P = .002, P = .002, and P = .009 respectively). At 1 yr, survey scores in opioid users were unchanged. There was no difference between groups in revision or failure rates. CONCLUSION: Sixty-four percent of patients who were using opioids prior to SCS reduced (n = 2) or eliminated opioid use (n = 29) at 1 yr postoperatively. Patients who eliminated opioid use or never used opioids had superior clinical outcomes to those who continued use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".