Cost-effectiveness of nodal observation versus completion lymphadenectomy in patients with melanoma and sentinel lymph node metastases.
Bibliographic record
Abstract
e22076 Background: The MSLT-II trial demonstrated no survival benefit of completion lymphadenectomy (CLND) compared to nodal observation (NO) and subsequent therapeutic lymphadenectomy (TLND) in the case of macroscopic nodal relapse in patients with melanoma and SLN metastases. NO avoids the upfront cost and morbidity of CLND. However, patients followed with NO must undergo intensive surveillance and if TLND is required, it is associated with a higher complication rate than CLND. The cost-effectiveness of NO versus CLND in light of data from MSLT-II has not been previously studied. Methods: A Markov model with a 10-year time horizon was constructed to simulate two hypothetical cohorts of patients with SLN metastases undergoing NO and subsequent TLND for nodal recurrence or upfront CLND. Transition probabilities between disease states were derived from the MSLT-II trial. Remaining parameters including complication rates and health state utilities were obtained from an extensive review of the literature. Direct health care system costs were obtained from published US Medicare cost data and the literature. Primary outcomes were cost and quality-adjusted life years (QALYs) saved. Incremental cost-effectiveness ratio (ICER) was used to compare treatment strategies. Sensitivity analysis was performed in order to evaluate model uncertainty. A threshold of acceptance of $100,000/QALY was used. Results: Total projected cost over the study period for CLND was $28,609.87, while that of NO was lower at $20,865.27, resulting in $7,744.60 saved for the NO treatment strategy. Ten-year utility was 4.840 for CLND compared to 5.379 for NO, resulting in a gain of 0.539 QALYs in the NO arm. The NO strategy is dominant in the model as it results in both cost-savings and a gain in health effects, with an average ICER of -$14,368.46/QALY gained. Conclusions: From the payer perspective, the strategy of NO compared to CLND in patients with melanoma and SLN metastases is associated with an improvement in health outcomes and reduction in cost. Taking into account MSLT-II trial data, this study demonstrates NO is more cost-effective than CLND.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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".