Cost-Effectiveness of Spinal Cord Stimulation Therapy in Management of Chronic Pain
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost-effectiveness of spinal cord stimulation (SCS) and conventional medical management (CMM) compared with CMM alone for patients with failed back surgery syndrome (FBSS), complex regional pain syndrome (CRPS), peripheral arterial disease (PAD), and refractory angina pectoris (RAP). DESIGN: Markov models were developed to evaluate the cost-effectiveness of SCS vs CMM alone from the perspective of a Canadian provincial Ministry of Health. Each model followed costs and outcomes in 6-month cycles. Health effects were expressed as quality-adjusted life years (QALYs). Costs were gathered from public sources and expressed in 2012 Canadian dollars (CAN$). Costs and effects were calculated over a 20-year time horizon and discounted at 3.5% annually, as suggested by the National Institute of Clinical Excellence. Cost-effectiveness was identified by deterministic and probabilistic sensitivity analysis (50,000 Monte-Carlo iterations). Outcome measures were: cost, QALY, incremental net monetary benefit (INMB), incremental cost-effectiveness ratio (ICER), expected value of perfect information (EVPI), and strategy selection frequency. RESULTS: The ICER for SCS was: CAN$ 9,293 (FBSS), CAN$ 11,216 (CRPS), CAN$ 9,319 (PAD), CAN$ 9,984 (RAP) per QALY gained, respectively. SCS provided the optimal economic path. The probability of SCS being cost-effective compared with CMM was 75-95% depending on pathology. SCS generates a positive INMB for treatment of pain syndromes. Sensitivity analyses demonstrated that results were robust to plausible variations in model costs and effectiveness inputs. Per-patient EVPI was low, indicating that gathering additional information for model parameters would not significantly impact results. CONCLUSION: SCS with CMM is cost-effective compared with CMM alone in the management of FBSS, CRPS, PAD, and RAP.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".