Spinal Cord Stimulation in Treatment of Chronic Benign Pain: Challenges in Treatment Planning and Present Status, a 22-Year Experience
Bibliographic record
Abstract
OBJECTIVE: To present an in-depth analysis of clinical predictors of outcome including age, sex, etiology of pain, type of electrodes used, duration of pain, duration of treatment, development of tolerance, employment status, activities of daily living, psychological status, and quality of life. Suggestions for treatment of low back pain with a predominant axial component are addressed. We analyzed the complications and proposed remedial measures to improve the effectiveness of this modality. METHODS: Study group consists of 410 patients (252 men, 58 women) with a mean age of 54 years and a mean follow-up period of 97.6 months. All patients were gated through a multidisciplinary pain clinic. The study was conducted over 22 years. RESULTS: The early success rate was 80% (328 patients), whereas the long-term success rate of internalized patients was 74.1% (243 patients) after the mean follow-up period of 97.6 months. Hardware-related complications included displaced or fractured electrodes, infection, and hardware malfunction. Etiologies demonstrating efficacy included failed back syndrome, peripheral vascular disease, angina pain, complex regional pain syndrome I and II, peripheral neuropathy, lower limb pain caused by multiple sclerosis. Age, sex, laterality of pain or number of surgeries before implant did not play a role in predicting outcome. The percentage of pain relief was inversely related to the time interval between pain onset and time of implantation. Radicular pain with axial component responded better to dual Pisces electrode or Specify-Lead implantation. CONCLUSION: Spinal cord stimulation can provide significant long-term pain relief with improved quality of life and employment. Results of this study will be effective in better defining prognostic factors and reducing complications leading to higher success rates with spinal cord stimulation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".