Ethics, economics, and patient-centered outcomes of spinal cord stimulation
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: With the widespread integration of spinal cord stimulation (SCS) into clinical practice, understanding its ethical, economic, and patient-centered implications has become increasingly important. This review critically examines recent evidence across these domains to illuminate challenges and opportunities for advancing transparent, ethical, patient-centered, and value-based neuromodulation practice. RECENT FINDINGS: Recent analyses reveal persistent challenges with bias, conflicts of interest, and selective outcome reporting in neuromodulation research. Studies demonstrate significant disparities in access to SCS across racial and socioeconomic groups and highlight new ethical considerations associated with artificial intelligence-enabled and informed treatment in neuromodulation. Contemporary randomized trials support clinically meaningful improvements in pain, functionality, psychological outcomes, and other patient-centered outcomes, although durability remains inconsistent because of the potential for therapy habituation or adverse events. Despite high initial upfront costs, evaluations of SCS cost-effectiveness across healthcare systems generally favor SCS over conventional medical management when assessed over multiyear periods, with differential economic benefits observed based on the type of waveform and type of national healthcare system. SUMMARY: While current evidence supports the clinical and long-term economic benefits of SCS in defined clinical contexts and indications, gaps in research transparency, equitable access to care, economic considerations, and durability of effectiveness persist.
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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 | 0.024 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".