Real‐world treatment practice in patients with advanced melanoma in the era before ipilimumab: results from the <scp>IMAGE</scp> study
Bibliographic record
Abstract
The therapeutic landscape for advanced melanoma has recently been transformed by several novel agents (immune checkpoint inhibitors and molecular-targeted agents). The prospective, multi-site, observational study IMAGE (ipilimumab: management of advanced melanoma in real practice) included a retrospective cohort to describe real-world treatment prior to approval of the immune checkpoint inhibitor ipilimumab. This retrospective cohort of patients, who started second-line/subsequent treatment (index therapy) for advanced melanoma within 3 years before ipilimumab approval, was selected randomly by chart review. Collected data included treatment history, patient outcomes, and healthcare resource utilization. All patients had ≥1 year of follow-up data. This analysis included 177 patients from Europe (69%) and North America (31%). The most common index therapies (used alone or in combination) were fotemustine (23%), dacarbazine (21%), temozolomide (14%), and platinum-based chemotherapy (14%). Most patients (89%) discontinued index treatment during the study period; the most common reason was disease progression (59%). Among patients with tumor assessment (153/177; 86%), 2% had complete response, 5% had partial response, and 12% had stable disease on last tumor assessment. At 1-year study follow-up, median progression-free survival was 2.6 months (95% confidence interval [CI], 2.1-2.9) and median overall survival was 8.8 months (95% CI, 6.5-9.7). During follow-up, 95% of the patients had healthcare visits for advanced melanoma, 74% of whom were hospitalized or admitted to a hospice facility. These results provide insights into patient care with advanced melanoma in the era before ipilimumab and may serve as a benchmark for new agents in future real-world studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".