Real World Outcomes in Patients with Advanced Melanoma Treated in Alberta, Canada: A Time-Era Based Analysis
Bibliographic record
Abstract
Immune checkpoint and MAP kinase pathway inhibitors can significantly improve long-term survival for patients with melanoma. There is limited real-world data of these regimens’ effectiveness. We retrospectively analyzed 402 patients with unresectable and metastatic melanoma between August 2013 and July 2020 treated with immune checkpoint inhibitors and MAP kinase pathway targeted therapy in Alberta, Canada. Overall survival (OS) was compared using Kaplan–Meier and Cox regression analyses. Subgroup survival outcomes were analyzed by first-line treatment regime and BRAF mutation status. Three treatment eras were defined based on drug access: prior to August 2013, August 2013 to November 2016, and November 2016 to July 2020. Across each era, there were improvements in median OS: 11.7 months, 15.9 months, and 33.6 months, respectively. Patients with BRAF mutant melanoma had improved median OS when they were treated with immunotherapy in the first line as opposed to targeted therapy (median OS not reached for immunotherapy versus 17.4 months with targeted treatment). Patients with BRAF wild-type melanomas had improved survival with ipilimumab and nivolumab versus those treated with a single-agent PD-1 inhibitor (median OS not reached and 21.2 months). Our real-world analysis confirms significant survival improvements with each subsequent introduction of novel therapies for advanced melanoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".