Laminoplasty compared to laminectomy and fusion for degenerative cervical myelopathy: a cost-utility analysis
Bibliographic record
Abstract
Background: Degenerative cervical myelopathy (DCM) is a leading cause of spinal cord dysfunction in adults. Posterior surgical options include laminoplasty (LP), a motion-preserving procedure, and laminectomy with fusion (LF), which offers stabilization but at higher cost. A cost-utility comparison of these approaches is lacking. Methods: We conducted a cost-utility analysis using a 5-year Markov state-transition model from a healthcare payer perspective. The study cohort was drawn from 3 multicenter prospective studies of patients with DCM. To reduce baseline confounding, patients who underwent LF or LP were matched 2:1 using propensity score matching. Quality-adjusted life years (QALYs) were derived from SF-6D utilities calculated from SF-36 scores at baseline and 12 months. Costs and transition probabilities were derived from meta-analysis and registry data. We calculated incremental cost-utility ratios (ICURs) and net monetary benefit (NMB) using a willingness-to-pay (WTP) threshold of $100,000 per QALY. Sensitivity analyses included deterministic variation and 1,000-iteration Monte Carlo microsimulation. Results: 240 matched patients were included (87 LP, 153 LF). Baseline mJOA, NDI and SF6D scores were similar between groups. In the base-case, LP cost $24,283, yielding 3.53 QALYs, while LF cost $35,902, yielding 3.72 QALYs, resulting in an incremental cost-utility ratio (ICUR) of $59,796 per QALY, favouring LF. In probabilistic microsimulation, LP had a mean cost of $24,579 (95% confidence interval (CI): $23,933-$25,225) and a mean NMB of $326,281 (95% CI: $322,672-$329,891), while LF cost $35,936 (95% CI: $35,583-$36,288) with a mean NMB of $333,189 (95% CI: $328,629-$337,748). LP was the optimal strategy in 47% of simulations versus 53% for LF. Conclusions: Both LP and LF can be cost-effective for DCM. While the base-case favored LF, probabilistic sensitivity analysis revealed no clear cost-utility advantage with comparable NMBs. Economic considerations alone should not drive surgical decision-making and treatment choice should be tailored to individual patient factors and local resource contexts.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".