Cost‐effectiveness of transoral robotic surgery versus (chemo)radiotherapy for early T classification oropharyngeal carcinoma: A cost‐utility analysis
Bibliographic record
Abstract
BACKGROUND: The present study is an economic evaluation comparing transoral robotic surgery (TORS) to (chemo)radiotherapy for the management of early T-classification oropharyngeal cancer. METHODS: A societal perspective was adopted. Treatment for TORS and (chemo)radiotherapy were modeled using decision analysis and recurrences were modeled over a 10 year horizon with a Markov model. Model parameters were derived from systematic review. Deterministic and probabilistic sensitivity analyses were used to test model robustness. RESULTS: TORS demonstrated a cost savings of $1366 and an increase of 0.25 quality-adjusted life years (QALYs) per case in comparison to (chemo)radiotherapy. TORS was sensitive to variations in adjuvant therapy, costs, utilities, complications, and recurrence rates in deterministic and probabilistic sensitivity analysis. In two-way sensitivity analysis, with increasing adjuvant therapy for TORS and decreasing concurrent chemotherapy for radiotherapy, TORS is decreasingly cost-effective. CONCLUSION: TORS is cost-effective for treatment of early oropharyngeal cancer. Case selection to minimize adjuvant therapy ensures cost-effective 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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".