Spinal cord stimulation for failed back surgery syndrome: A decision-analytic model and cost-effectiveness analysis
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to develop a decision-analytic model to assess the cost-effectiveness of spinal cord stimulation (SCS), relative to nonsurgical conventional medical management (CMM), for patients with failed back surgery syndrome (FBSS). METHODS: A decision tree and Markov model were developed to synthesize evidence on both health-care costs and outcomes for patients with FBSS. Outcome data of SCS and CMM were sourced from 2-year follow-up data of two randomized controlled trials (RCTs). Treatment effects were measured as levels of pain relief. Short- and long-term health-care costs were obtained from a detailed Canadian costing study in FBSS patients. Results are presented as incremental cost per quality adjusted life year (QALY) and expressed in 2003 Euros. Costs were discounted at 6 percent and outcomes at 1.5 percent. RESULTS: Over the lifetime of the patient, SCS was dominant (i.e., SCS is cost-saving and gives more health gain relative to CMM); a finding that was robust across sensitivity analyses. At a 2-year time horizon, SCS gave more health gain but at an increased cost relative to CMM. Given the uncertainty in effectiveness and cost parameters, the 2-year cost-effectiveness of SCS ranged from 30,370 Euros in the base case to 63,511 Euros in the worst-case scenario. CONCLUSIONS: SCS was found to be both more effective and less costly than CMM, over the lifetime of a patient. In the short-term, although SCS is potentially cost-effective, the model results are highly sensitive to the choice of input parameters. Further empirical data are required to improve the precision in the estimation of short-term cost-effectiveness.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".