Outcomes of Patients with Metastatic Melanoma Treated with Molecularly Targeted Agents in Phase I Clinical Trials
Bibliographic record
Abstract
INTRODUCTION: First-line treatment options utilizing chemotherapy and cytokine-based treatments for patients with metastatic melanoma (MM) are unsatisfactory. We analyzed the clinical outcomes of patients with MM treated in phase I trials of novel agents. We hypothesized that patients included in phase I clinical trials did not have worse outcomes than with the chemotherapy and cytokine-based first-line treatment. METHODS: Data of patients with MM treated at The Drug Development Unit between 2004 and 2010 were collected. The response rate (RR) and time to progression (TTP) for first-line therapy were compared to those of phase I trial therapy. Patients acted as their own controls for statistical analyses. RESULTS: Sixty-five patients were treated in 31 phase I trials. First-line treatment included dacarbazine or temozolomide in 58 (89%) cases and interferon-α in 5 patients (8%) and cisplatin-based treatment in 2 patients (3%). There was no significant difference in either the RR (11 vs. 14%, p = 0.87) or TTP (90 vs. 53 days, p = 0.15) in patients treated with first-line treatment versus phase I treatment, respectively. CONCLUSION: Phase I clinical trials of molecularly targeted agents show clinical activity that is not dissimilar to that of treatment with existing chemotherapy and cytokine-based treatment.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".