Spinal Cord Stimulation Real-World Outcomes: A 24-Month Longitudinal Cohort Study
Bibliographic record
Abstract
Background/Objectives: Spinal cord stimulation (SCS) is an established therapy for chronic pain, but uncertainties remain regarding long-term real-world outcomes and the role of standardized selection pathways. This study aimed to evaluate real-world, longitudinal outcomes of SCS over 24 months within a structured clinical pathway, focusing on pain intensity, neuropathic symptoms, and health-related quality of life. Methods: A single-center, retrospective observational cohort study was conducted at the Fondazione Istituto G. Giglio (Cefalù, Italy). Data were drawn from the continuing, prospective institutional “SCS Pathway” and included consecutive patients implanted between May 2021 and September 2024. Eligible patients were ≥18 years of age with chronic pain refractory to conventional medical management. Outcomes included pain intensity (VAS, visual analog scale), neuropathic features (DN4, douleur neuropathique 4), and health-related quality of life (EQ-5D, EuroQol 5 Dimensions), assessed at baseline and 3, 6, 12, 18, and 24 months post-implantation. Multilevel models with full information maximum likelihood (FIML) were applied to repeated measures. Results: Seventy-six patients were included (mean age 67.3 ± 10.3 years; 39.5% female). The most frequent diagnoses were post-surgical pain syndrome (42.1%, 32/76) and chronic back and leg pain (40.8%, 31/76). 42.1% (32/76) had previous spine surgery, and 78.9% (60/76) reported neuropathic pain. Across 452 observations, mean VAS scores decreased from 7.9 ± 0.7 at baseline to 3.1 ± 1.1 at 3 months (61% reduction, p < 0.001), with sustained benefit at 24 months (4.5 ± 1.5; 43% reduction, p < 0.001). DN4 scores improved from 7.4 ± 0.8 to 3.2 ± 1.0 at 3 months (56% reduction, p < 0.001), with persistent decreases at 24 months (4.2 ± 1.2; 43% reduction, p < 0.001). EQ-5D improved from 22.8 ± 6.6 at baseline to 70.2 ± 10.6 at 3 months (increase of 208%, p < 0.001), with clinically meaningful gains sustained at 24 months (55.4 ± 13.7, increase of 143%, p < 0.001). Conclusions: In this real-world cohort, SCS therapy results in sustained, clinically significant improvements in pain, neuropathic symptoms, and quality of life. Findings highlight the value of structured selection and follow-up pathways. These data provide a benchmark for multicenter studies linking standardized referral frameworks to long-term, patient-centered outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".