Development of ipilimumab: a novel immunotherapeutic approach for the treatment of advanced melanoma
Bibliographic record
Abstract
The immunotherapeutic agent ipilimumab has helped address a significant unmet need in the treatment of advanced melanoma. Ipilimumab is a fully human monoclonal antibody that targets cytotoxic T-lymphocyte antigen-4 (CTLA-4), thereby augmenting antitumor immune responses. After decades in which a number of clinical trials were conducted, ipilimumab was the first therapy to improve overall survival in a randomized, controlled phase III trial of patients with advanced melanoma. These results led to the regulatory approval of ipilimumab at 3 mg/kg for the treatment of unresectable or metastatic melanoma. More than 17,000 patients worldwide have received ipilimumab, either as a commercial drug at 3 mg/kg or in clinical trials and expanded access programs at different doses. Consistent with its proposed mechanism of action, the most common toxicities associated with ipilimumab therapy are inflammatory in nature. These immune-related adverse events were mostly reversible when effective treatment guidelines were followed. Importantly, long-term follow-up of patients who received ipilimumab in a phase III trial showed that 24% survived at least two years, and in phase II studies, a proportion of patients survived at least five years. Evaluation of ipilimumab is ongoing in the adjuvant setting for melanoma, and for advanced disease in nonsmall cell lung, small cell lung, prostate, ovarian, and gastric cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".