Patient and population impacts of multigene panel and pembrolizumab coverage in metastatic melanoma
Bibliographic record
Abstract
BACKGROUND: Targeted treatment or immunotherapy may yield increased, durable responses for melanoma patients. Whether patient-level benefits translate to population health is unknown. This study sought to estimate patient and population impacts of a cancer control policy that reimbursed multigene panel testing and pembrolizumab for metastatic melanoma in British Columbia, Canada. METHODS: This retrospective study examined a population-based cohort of 721 adults diagnosed with metastatic melanoma in British Columbia who received single or multigene testing between 2013 and 2018. We determined patient-level policy impacts using 1:1 genetic algorithm matching of policy-affected patients with historical control patients and Kaplan-Meier analysis and inverse probability of censoring weighted regression of 2-year health-care costs and survival times. For population-level effects, we applied interrupted time-series analysis on monthly health-care system expenditures and mortality rates, estimating autoregressive integrated moving average and generalized least squares Poisson regressions. RESULTS: Matched cohort analysis (control patients, n = 154; intervention patients, n = 154) found mean cumulative patient-level cost increases of CAD$53 963 (95% confidence interval [CI] = $35 641 to $72 621; P < .001) and increased survival times of 111 days (95% CI = 44 to 166 days; P < .001) over 2 years. Higher patient-level systemic therapy spending of CAD$48 890 (95% CI = $31 110 to $66 910; P < .001) drove overall cost differences. Population-interrupted time-series analysis detected an immediate, sustained increase in mean monthly health-care expenditures of CAD$1921 (95% CI = $935 to $2908; P < .001) per patient. Higher overall spending did not coincide with population-level mortality changes. CONCLUSIONS: The policy of reimbursing multigene testing and pembrolizumab produced patient survival improvements, but selectivity of response prevented population mortality improvement. Health-care system costs statistically significantly increased at the patient and population levels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".