A population-based study of the treatment effect of first-line ipilimumab for metastatic or unresectable melanoma
Bibliographic record
Abstract
Ipilimumab is an anti-CTLA4 monoclonal antibody with demonstrated efficacy for metastatic melanoma in randomized controlled trials, including in the first-line setting. Population-based outcomes directly compared with historic chemotherapy treatment in metastatic or unresectable melanoma are lacking. Using population-based data from the province of Ontario, the benefit of first-line ipilimumab was estimated by comparing outcomes of patients treated with first-line dacarbazine over the period 2007-2009 with patients treated over the period 2010-2015 with first-line ipilimumab. Cutaneous and noncutaneous cases were included. The administrative data set utilized was high-dimensional; meaning, there was a large number of variables relative to the sample size. To adjust for important confounders among the many available variables, we utilized a double-selection method, a modified machine learning algorithm to extract the important variables that were related to both survival times and treatment usage. Time-dependent treatment modeling was utilized. Among the 2793 melanoma patients receiving palliative treatment (systemic therapy, surgery, or radiation) in Ontario (2007-2015), there were 289 patients treated with first-line ipilimumab (2010-2015) and 175 patients treated with first-line dacarbazine (2007-2009). For first-line ipilimumab, the adjusted hazard ratio compared with dacarbazine for overall survival (OS) was 0.63 (95% confidence interval: 0.47-0.84) with a 1-year survival of 46.5 versus 18.9% with dacarbazine. In subgroup analysis, ipilimumab was associated with improved OS across groups (age, sex, comorbidity, income quintile, previous interferon). First-line ipilimumab was found to have a significant OS benefit compared with historical controls in a population including those patients not routinely included in clinical trials. The treatment effect was similar to randomized controlled trials, suggesting a meaningful benefit when utilized in a population-based setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".