Overall Survival Should Be the Primary Endpoint in Clinical Trials for Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
An article in a recent edition of Current Oncology explored the validation of progression-free survival (pfs) as an endpoint in clinical trials of antineoplastic agents for metastatic colorectal cancer, metastatic renal cell carcinoma, and ovarian cancer. The support for pfs as a surrogate endpoint for overall survival (os) was elucidated. As with the aforementioned tumour types, advanced non-small-cell lung cancer (nsclc) has seen a rise in active agents since the year 2000. Those agents range from improved cytotoxics such as pemetrexed, to targeted therapies such as tyrosine kinase inhibitors of the epidermal growth factor receptor and agents that target the EML4-ALK gene mutation. More recently, it has also become apparent that histology plays an important role in the response to and outcomes of treatment. With the therapeutic options for patients with advanced nsclc increasing, concerns are being raised that the efficacy of drugs measured by os may be diluted in clinical trials, thereby underestimating their true clinical benefit. That possibility, together with the need to have efficacious drugs available to patients earlier, has resulted in the search for a surrogate to the os endpoint in advanced nsclc. The present article follows up the recent article on pfs as a surrogate. Although advances in identifying pfs as a valid surrogate endpoint for os have been made in other tumour types, in advanced nsclc, such surrogacy has not been formally validated. Until it has, os should remain the primary endpoint of clinical trials in advanced nsclc.
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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 | 0.249 | 0.316 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".