A population-based study of the treatment effect of first-line ipilimumab for metastatic or unresectable melanoma
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it