Nivolumab in Metastatic Melanoma: Good Efficacy and Tolerability in Elderly Patients
Bibliographic record
Abstract
Background: Nivolumab is an anti–PD-1 antibody that restores the antitumour immune function of T cells, blocking the binding of PD-1 with its ligand PD-L1. PD-1 is expressed on T cells and interacts with PD-L1 on tumour cells. The PD-1–PD-L1 link inhibits T cell activation. In metastatic melanoma, PD-1–PD-L1 binding plays a critical role, and the advent of the immune checkpoint inhibitor nivolumab has delivered new and effective treatment options with proven clinical benefit. In the present study, we evaluated the efficacy of nivolumab in elderly patients with metastatic melanoma. Methods: The study enrolled 55 elderly patients (75 years of age and older) with a diagnosis of metastatic melanoma. Primary endpoints of the study were progression-free survival (PFS) and the objective response rate; secondary endpoints were overall survival, reduction in serum lactate dehydrogenase (LDH) from before to after treatment, and tolerability. Results: Nivolumab was well tolerated and resulted in good disease control, with a manageable toxicity profile and significant clinical benefit. The duration of PFS was 5.1 months (95% confidence interval: 3.5 months to 6.8 months). A significant correlation was observed between reduction in serum LDH and PFS: 0.60 (95% confidence interval: 0.28 to 0.86; p = 0.002). Conclusions: Nivolumab is an immunotherapy treatment that has proved to be an effective and well-tolerated therapeutic option in elderly patients with metastatic melanoma.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".