Spinal Cord Stimulation for Failed Back Surgery Syndrome: A Decision Analytic Model & Cost Effectiveness Analysis
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it