FDA Approval Summary: Nivolumab with Ipilimumab and Chemotherapy for Metastatic Non–small Cell Lung Cancer, A Collaborative Project Orbis Review
Bibliographic record
Abstract
Abstract On May 26, 2020, the FDA approved nivolumab with ipilimumab and two cycles of platinum-doublet chemotherapy as first-line treatment for patients with metastatic or recurrent non–small cell lung cancer (NSCLC), with no EGFR or anaplastic lymphoma kinase (ALK) genomic tumor aberrations. The approval was based on results from Study CA2099LA (CheckMate 9LA), an open-label trial in which 719 patients with NSCLC were randomized to receive nivolumab with ipilimumab and two cycles of chemotherapy (n = 361) or four cycles of platinum-doublet chemotherapy (n = 358). Overall survival (OS) was improved for patients who received nivolumab with ipilimumab and chemotherapy, with a median OS of 14.1 months [95% confidence interval (CI), 13.2–16.2] compared with 10.7 months (95% CI, 9.5–12.5) for patients who received chemotherapy (HR, 0.69; 96.71% CI, 0.55–0.87; P = 0.0006). Progression-free survival and overall response rate per blinded independent central review were also statistically significant. This was the first NSCLC application reviewed under FDA's Project Orbis, in collaboration with Singapore's Health Sciences Authority, Australia's Therapeutic Goods Administration, and Health Canada. The benefit–risk analysis supports FDA's approval of nivolumab with ipilimumab and chemotherapy.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".