Evidence-based consensus guidelines on patient selection and trial stimulation for spinal cord stimulation therapy for chronic non-cancer pain
Bibliographic record
Abstract
Spinal cord stimulation (SCS) has demonstrated effectiveness for neuropathic pain. Unfortunately, some patients report inadequate long-term pain relief. Patient selection is emphasized for this therapy; however, the prognostic capabilities and deployment strategies of existing selection techniques, including an SCS trial, have been questioned. After approval by the Board of Directors of the American Society of Regional Anesthesia and Pain Medicine, a steering committee was formed to develop evidence-based guidelines for patient selection and the role of an SCS trial. Representatives of professional organizations with clinical expertize were invited to participate as committee members. A comprehensive literature review was carried out by the steering committee, and the results organized into narrative reports, which were circulated to all the committee members. Individual statements and recommendations within each of seven sections were formulated by the steering committee and circulated to members for voting. We used a modified Delphi method wherein drafts were circulated to each member in a blinded fashion for voting. Comments were incorporated in the subsequent revisions, which were recirculated for voting to achieve consensus. Seven sections with a total of 39 recommendations were approved with 100% consensus from all the members. Sections included definitions and terminology of SCS trial; benefits of SCS trial; screening for psychosocial characteristics; patient perceptions on SCS therapy and the use of trial; other patient predictors of SCS therapy; conduct of SCS trials; and evaluation of SCS trials including minimum criteria for success. Recommendations included that SCS trial should be performed before a definitive SCS implant except in anginal pain (grade B). All patients must be screened with an objective validated instrument for psychosocial factors, and this must include depression (grade B). Despite some limitations, a trial helps patient selection and provides patients with an opportunity to experience the therapy. These recommendations are expected to guide practicing physicians and other stakeholders and should not be mistaken as practice standards. Physicians should continue to make their best judgment based on individual patient considerations and preferences.
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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 | 0.383 | 0.401 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.019 | 0.013 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".