FDA Approval Summary: Osimertinib for Adjuvant Treatment of Surgically Resected Non–Small Cell Lung Cancer, a Collaborative Project Orbis Review
Bibliographic record
Abstract
Abstract On December 18, 2020, the FDA approved osimertinib as adjuvant therapy in patients with non–small cell lung cancer (NSCLC) whose tumors have EGFR exon 19 deletions or exon 21 (L858R) mutations, as detected by an FDA-approved test. The approval was based on the ADAURA study, in which 682 patients with NSCLC were randomized to receive osimertinib (n = 339) or placebo (n = 343). Disease-free survival (DFS) in the overall population (stage IB–IIIA) was improved for patients who received osimertinib, with an HR of 0.20; 95% confidence interval (CI), 0.15–0.27; P < 0.0001. Median DFS was not reached for the osimertinib arm compared with 27.5 months (95% CI, 22.0–35.0) for patients receiving placebo. Overall survival data were not mature at the time of the approval. This application was reviewed under FDA's Project Orbis, in collaboration with Australia Therapeutic Goods Administration, Brazil ANVISA, Health Canada, Singapore Health Sciences Authority, Switzerland Swissmedic, and the United Kingdom Medicines and Healthcare products Regulatory Agency. This is the first targeted therapy adjuvant approval for NSCLC and has practice-changing implications.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".