Real-world comparative effectiveness of second-line ipilimumab for metastatic melanoma: a population-based cohort study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: For novel cancer treatments, effectiveness in clinical practice is not always aligned with clinical efficacy results. As such it is important to understand a treatment's real-world effectiveness. We examined real-world population-based comparative effectiveness of second-line ipilimumab versus non-ipilimumab treatments (chemotherapy or targeted treatments). METHODS: We used a cohort of melanoma patients receiving systemic treatment for advanced disease since April 2005 from Ontario, Canada. Patients were identified from provincial drug databases and the Ontario Cancer Registry who received second-line ipilimumab from 2012 to 2015 (treated) or second-line non-ipilimumab treatment prior to 2012 (historical controls). Historical controls were chosen, to permit the most direct comparison to pivotal trial findings. The cohort was linked to administrative databases to identify baseline characteristics and outcomes. Kaplan-Meier curves and multivariable Cox regression models were used to assess overall survival (OS). Observed potential confounders were adjusted for using inverse probability of treatment weighting (IPTW). RESULTS: We identified 329 patients with metastatic melanoma (MM) who had received second-line treatments (189 treated; 140 controls). Patients receiving second-line ipilimumab were older (61.7 years vs 55.2 years) compared to historical controls. Median OS were 6.9 (95% CI: 5.4-8.3) and 4.95 (4.3-6.0) months for ipilimumab and controls, respectively. The crude 1-year, 2-year, and 3-year OS probabilities were 34.3% (27-41%), 20.6% (15-27%), and 15.2% (9.6-21%) for ipilimumab and 17.1% (11-23%), 7.1% (2.9-11%), and 4.7% (1.2-8.2%) for controls. Ipilimumab was associated with improved OS (IPTW HR = 0.62; 95% CI: 0.49-0.78; p < 0.0001). CONCLUSIONS: This real-world analysis suggests second-line ipilimumab is associated with an improvement in OS for MM patients in routine practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".