Cost-utility of two minimally-invasive surgical techniques for operable oropharyngeal cancer: Transoral robotic surgery versus transoral laser microsurgery
Bibliographic record
Abstract
Abstract Importance Transoral robotic surgery (TORS) and transoral laser micro-surgery (TLM) are two different but competing minimally invasive techniques to surgically remove operable oropharyngeal squamous cell cancers (OPSCC). As of now, no comparative analysis as to the cost-utility of these techniques exists. Objective Recent population-level data suggest for TORS and TLM equivalent tumor control, but different total costs, need for adjuvant chemoradiation, and learning curves. Therefore, the objective of this study was to compare TORS and TLM from the cost-utility (C/U) point of view using a decision-analytical model from a Swiss hospital perspective. Design Our decision-analytical model combines decision trees and a Markov model to compare TORS and TLM strategies. Model parameters were quantified using available literature, original cost data from two Swiss university tertiary referral centers, and utilities elicited directly from a Swiss population sample using standard gamble. C/U and sensitivity analyses were used to generate results and gauge model robustness. Setting Swiss hospital perspective Intervention Cost-utility analysis Main outcome measure Comparative cost-utility data from TLM and TORS Results In the base case analysis TLM dominates TORS. This advantage remains robust, even if the costs for TORS would reduce by up to 25%. TORS begins to dominate TLM, if less than 59,7% patients require adjuvant treatment (pTorsAlone>0.407), whereby in an interval between 55%-62% (pTorsAlone 0.38-0.45) cost effectiveness of TORS is sensitive to the prescription of adjuvant CRT. Also, exceeding 29% of TLM patients requiring a re-operation for inadequate margins renders TORS more cost-effective. Conclusion TLM is more cost-effective than TORS. However, this advantage is sensitive to various parameters i.e. the number of re-operations and adjuvant treatment. Key points Question Compare cost-utility of TORS versus TLM Findings In the base case analysis TLM dominates TORS, even if the costs for TORS would reduce by up to 25%. TORS begins to dominate TLM, if less than 59,7% patients require adjuvant treatment, whereby in an interval between 55%-62% cost effectiveness of TORS is sensitive to the prescription of adjuvant CRT. Exceeding 29% of TLM patients requiring a re-operation for inadequate margins renders TORS more cost-effective. Meaning TLM is more cost-effective than TORS. However, this advantage is sensitive to the number of re-operations and adjuvant treatment
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.004 | 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".