Role of patient selection and trial stimulation for spinal cord stimulation therapy for chronic non-cancer pain: a comprehensive narrative review
Bibliographic record
Abstract
BACKGROUND/IMPORTANCE: Patient selection for spinal cord stimulation (SCS) therapy is crucial and is traditionally performed with clinical selection followed by a screening trial. The factors influencing patient selection and the importance of trialing have not been systematically evaluated. OBJECTIVE: We report a narrative review conducted to synthesize evidence regarding patient selection and the role of SCS trials. EVIDENCE REVIEW: Medline, EMBASE and Cochrane databases were searched for reports (any design) of SCS in adult patients, from their inception until March 30, 2022. Study selection and data extraction were carried out using DistillerSR. Data were organized into tables and narrative summaries, categorized by study design. Importance of patient variables and trialing was considered by looking at their influence on the long-term therapy success. FINDINGS: Among 7321 citations, 201 reports consisting of 60 systematic reviews, 36 randomized controlled trials (RCTs), 41 observational studies (OSs), 51 registry-based reports, and 13 case reports on complications during trialing were included. Based on RCTs and OSs, the median trial success rate was 72% and 82%, and therapy success was 65% and 61% at 12 months, respectively. Although several psychological and non-psychological determinants have been investigated, studies do not report a consistent approach to patient selection. Among psychological factors, untreated depression was associated with poor long-term outcomes, but the effect of others was inconsistent. Most RCTs except for chronic angina involved trialing and only one RCT compared patient selection with or without trial. The median (range) trial duration was 10 (0-30) and 7 (0-56) days among RCTs and OSs, respectively. CONCLUSIONS: Due to lack of a consistent approach to identify responders for SCS therapy, trialing complements patient selection to exclude patients who do not find the therapy helpful and/or intolerant of the SCS system. However, more rigorous and large studies are necessary to better evaluate its role.
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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 | 0.037 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".