Cost‐effectiveness analysis of staging strategies in patients with regionally metastatic melanoma
Bibliographic record
Abstract
PURPOSE: Variability exists regarding optimal staging for node-positive melanoma. Options include combinations of physical examination (PE), radiography, computed tomography (CT), and positron emission tomography (PET). Cost-effectiveness of regimens has never been investigated. METHODS: A modeled cost-effectiveness analysis was performed to examine the cost per surgery performed and per accurate diagnosis achieved with three staging regimens (PE/chest radiography, CT, PET/CT) for node-positive melanoma. Incremental cost-effectiveness ratios were used to compare regimens. Deterministic and probabilistic sensitivity analyses were undertaken to address variation in parameters. Costs are direct from the perspective of the Canadian single-payer system and 2012 valuations. RESULTS: Staging with PE/radiography is the least cost-effective option, resulting in greater costs than CT alone, and fewer accurate diagnoses. Compared to CT alone, PET/CT incurs greater incremental cost ($902.81CAD), but results in 4% fewer lymphadenectomies and 4% more accurate diagnoses. PET/CT costs $22,570.25CAD for each additional accurate diagnosis achieved compared to CT alone. Sensitivity analyses demonstrate that the optimal staging strategy is influenced by diagnostic test characteristics and the willingness-to-pay threshold, but robust to other varied parameters. CONCLUSIONS: PE/radiography appears to be the least cost-effective staging regimen. The benefit of PET/CT over CT alone depends on a health system's priorities and willingness-to-pay.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".