Bending the Cost Curve—Establishing Value in Spine Surgery
Bibliographic record
Abstract
BACKGROUND: As publically promoted by all stakeholders in health care reform, prospective outcomes registry platforms lie at the center of all current evidence-driven value-based models. OBJECTIVE: To demonstrate the variability in outcomes and cost at population level and individual patient level for patients undergoing spine surgery for degenerative diseases. METHODS: Retrospective analysis of prospective longitudinal spine registry data was conducted. Baseline and postoperative 1-year patient-reported outcomes were recorded. Previously published minimal clinically important difference for Oswestry Disability Index (14.9) was used. Back-related resource utilization and quality-adjusted life years (QALYs) were assessed. Variations in outcomes and cost were analyzed at population level and at the individual patient level. RESULTS: A total of 1454 patients were analyzed. There was significant improvement in patient-reported outcomes at postoperative 1 year ( P < .0001). For patients demonstrating health benefit at population level, 12.5%, n = 182 of patients experienced no gain from surgery and 38%, n = 554 failed to achieve minimal clinically important difference. Mean 1-year QALY-gained was 0.29; 18% of patients failed to report gain in QALY. For patients with 2-year follow-up, surgery resulted in 0.62 QALY-gained at average direct cost of $28 953. A wide variation in both QALY-gained and cost was observed. CONCLUSION: Spine treatments that on average are cost-effective may have wide variability in value at the individual patient level. The variability demonstrated here represents an opportunity, through registries, to identify specific care that may be less effective, and refine patient-specific care delivery and indications to drive overall group-level treatment value. Understanding value of spine care at an individualized as well as population level will allow clinicians, and eventually payers, to better target resources for improving care for nonresponders, ultimately driving up the average health for the whole population.
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How this classification was reachedexpand
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 | 0.031 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".