Understanding Real-World Treatment Patterns and Clinical Outcomes among Metastatic Melanoma Patients in Alberta, Canada
Bibliographic record
Abstract
Immunotherapy and targeted therapies have been shown to considerably improve long-term survival outcomes in metastatic melanoma patients. Real-world evidence on the uptake of novel therapies and outcomes for this patient population in Canada are limited. We conducted a population-based retrospective cohort study of all metastatic melanoma patients diagnosed in Alberta, Canada (2015–2018) using electronic medical records and administrative data. Information on BRAF testing for patients diagnosed in 2017 or 2018 was obtained through chart abstraction. In total, 434 metastatic melanoma patients were included, of which 110 (25.3%) were de novo metastatic cases. The median age at diagnosis was 66 years (IQR: 57–76) and 70.0% were men. BRAF testing was completed for the majority of patients (88.7%). Among all patients, 60.4%, 19.1%, and 6.0% initiated first-line, second-line, and third-line systemic therapy. The most common therapies were anti-PD-1 and targeted therapies. The two-year survival probability from first-line therapy, second-line therapy, and third-line therapy was 0.50 (95% CI: 0.44–0.57), 0.26 (95% CI: 0.17–0.40), and 0.14 (95% CI: 0.40–0.46), respectively. In the first-line setting, survival was highest for patients that received ipilimumab or ipilimumab plus nivolumab, while targeted therapy had the highest survival in the second-line setting. This study indicates that novel therapies improve survival in the real world but a considerable proportion of patients do not receive treatment with systemic therapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".