Budget Impact Analysis of Minimally Invasive versus Open Transforaminal Lumbar Interbody Fusion for Lumbar Degenerative Disease: A European Hospital Perspective
Bibliographic record
Abstract
Purpose: When traditional therapies fail to provide relief from debilitating lower back pain, surgeries such as transforaminal lumbar interbody fusion (TLIF) may be required. This budget impact analysis (BIA) compared minimally-invasive (MI)-TLIF versus open (O)-TLIF for single-level fusion from an Italian hospital perspective. Methods: The BIA compared costs of 100 MI-TLIF and 100 O-TLIF procedures from an Italian hospital perspective over a one-year time horizon. The base case included costs for length of hospital stay (LOS), blood loss, and sterilizing surgical trays. The scenario analysis also included operating room (OR) time and complication costs. Base case inputs were from the Miller et al meta-analysis; scenario analysis inputs were from the Hammad et al meta-analysis. The device costs for MI-TLIF and O-TLIF procedures were from Italian tender prices for Viper Prime™ System and Expedium™ Spine System, respectively. Results: Base case deterministic analysis results showed cost savings of €207,370 for MI-TLIF compared with O-TLIF. MI-TLIF costs were lower for LOS (€215,277), transfusion for blood loss (€16,881), and surgical tray sterilization (€28,232), whereas device costs were lower for O-TLIF (€53,020). The probabilistic result was similar, with MI-TLIF resulting in savings of €211,026 (95% credible interval [CR]: €208,725 - €213,327). All 1000 base case probabilistic sensitivity analysis runs were cost saving. Deterministic scenario analysis results showed cost savings of €166,719 for MI-TLIF. MI-TLIF costs were lower for LOS (€190,813), transfusion for blood loss (€16,881), surgical tray sterilization (€28,232), and complications (€2076), whereas O-TLIF costs were lower for OR time (€18,263) and devices used (€53,020). Conclusion: Despite the increase incremental cost for medical device innovation and OR time, this study demonstrates the economic savings of MI-TLIF compared to O-TLIF from a European hospital perspective. The findings will be useful to policy and hospital decision makers in assessing purchasing, funding and reimbursement decisions.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".