Making a case for high‐volume robotic surgery centers: A cost‐effectiveness analysis of transoral robotic surgery
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cost-effectiveness of transoral robotic surgery (TORS) compared to intensity-modulated radiotherapy (IMRT) for early stage (T1-2, N0, M0) oropharyngeal squamous cell carcinoma (OPSCC). PATIENTS AND METHODS: A Markov decision tree model with a 5-year time horizon was developed. Comparative groups were: i) TORS with concurrent ipsilateral neck dissection +/- adjunctive IMRT, and ii) primary IMRT. Primary outcome was cost/quality adjusted life year (QALY). Perspective was the United States third party payer. Costs and effects were discounted at a rate of 3.5%. A threshold and probabilistic sensitivity analysis were performed. RESULTS: TORS strategy cost $30,992 and provided 4.81 QALYs/patient. The IMRT strategy cost $26,033 and provided a total of 4.78 QALYs/patient. The incremental cost effectiveness ratio for TORS vs. IMRT in the reference case was $165,300/QALY. The probability that TORS is cost-effective compared to IMRT at a maximum willingness-to-pay threshold of $50,000/QALY is 42%. CONCLUSION: An IMRT strategy for management of early stage OPSCC is more likely to be cost-effective compared to TORS. To improve the value of TORS for early stage OPSCC, consolidating TORS procedures to create high-volume centers of excellence may be a potential strategy to increase incremental effectiveness and reduce incremental costs. J. Surg. Oncol. 2015 111:155-163. © 2015 Wiley Periodicals, Inc.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| 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".