Elective neck dissection versus positron emission tomography–computed tomography–guided management of the neck in clinically node‐negative early oral cavity cancer: A cost–utility analysis
Bibliographic record
Abstract
BACKGROUND: In early oral cavity cancer, elective neck dissection (END) for the clinically node-negative (cN0) neck improves survival compared with observation. This paradigm has been challenged recently by the use of positron emission tomography-computed tomography (PET-CT) imaging in the cN0 neck. To inform this debate, we performed an economic evaluation comparing PET-CT-guided therapy with routine END in the cN0 neck. METHODS: Patients with T1-2N0 lateralized oral tongue cancer were analyzed. A Markov model over a 40-year time horizon simulated treatment, disease recurrence, and survival from a US health care payer perspective. Model parameters were derived from a review of the literature. RESULTS: The END strategy was dominant, with a cost savings of $1576.30 USD, an increase of 0.055 quality-adjusted life years (QALYs), a net monetary benefit of $4303 USD, and a 0.22 life-year advantage. END was sensitive to variation in cost and utilities in deterministic and probabilistic sensitivity analyses. PET-CT became the preferred strategy when decreasing occult nodal disease to 18% and increasing the negative predictive value (NPV) of PET-CT to 89% in 1-way sensitivity analyses. In probabilistic sensitivity analysis, assuming a cost effectiveness threshold of $50,000 USD/QALY, END was dominant in 64% of simulations and cost effective in 69.8%. CONCLUSION: END is a cost-effective strategy compared with PET-CT in patients who have node-negative oral cancer. Although lower PET standardized uptake value thresholds would result in fewer false negatives and improved NPV, it is still uncertain that PET-CT would be cost effective, as this would likely result in more false positive tests.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 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".