Multicolumn spinal cord stimulation for predominant back pain in failed back surgery syndrome patients: a multicenter randomized controlled trial
Bibliographic record
Abstract
Despite optimal medical management (OMM), low back pain (LBP) can be disabling, particularly after spinal surgery. Spinal cord stimulation (SCS) is effective in reducing neuropathic leg pain; however, evidence is limited for LBP. This prospective, open-label, parallel-group trial randomized (1:1) failed back surgery syndrome (FBSS) patients with predominant LBP to SCS plus OMM (SCS group) or OMM alone (OMM group) at 28 sites in Europe and the Americas. If trial stimulation was successful, a multicolumn SCS system was implanted. Outcomes were assessed at baseline (before randomization) and at 1, 3, 6, and 12 months after randomization. Patients could change treatment groups at 6 months. The primary outcome was the proportion of patients with ≥50% reduction in LBP (responder) at 6 months. Secondary outcomes included change in pain intensity, functional disability, and health-related quality of life (HRQoL). The results are posted at ClinicalTrials.gov under registration number NCT01697358. In the intent-to-treat analysis, there were more responders in the SCS group than in the OMM group (13.6%, 15/110 vs 4.6%, 5/108, difference 9% with 95% confidence interval 0.6%-17.5%, P = 0.036) at 6 months. The SCS group improved in all secondary outcomes compared with the OMM group. The OMM group only improved in HRQoL. In the SCS group, 17.6% (18/102) experienced SCS-related adverse events through 6 months, with 11.8% (12/102) requiring surgical reintervention. Adding multicolumn SCS to OMM improved pain relief, HRQoL, and function in a traditionally difficult-to-treat population of failed back surgery syndrome patients with predominant LBP. Improvements were sustained at 12 and 24 months.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".