Position of an international panel of lung cancer experts on the decision for expansion of approval for pembrolizumab in advanced non-small-cell lung cancer with a PD-L1 expression level of ≥1% by the USA Food and Drug Administration
Bibliographic record
Abstract
Immune-checkpoint inhibitors (ICIs) have transformed the therapeutic landscape of advanced non-small-cell lung cancer (NSCLC) and now represent the new first-line standard of care (SoC), either in combination with platinum-based chemotherapy, achieving a survival benefit independent of histology and programmed death ligand-1 (PD-L1) expression levels [1–4], or as monotherapy in patients whose tumors express PD-L1 in ≥50% of the tumor cells [5, 6]. Recently, the phase III KEYNOTE 042 trial reported that pembrolizumab monotherapy (200 mg every 3 weeks for up to 35 cycles) in patients with a PD-L1 tumor proportion score (TPS) of at least 1% significantly improved overall survival (OS) compared with investigators’ choice of platinum-based chemotherapy [16.7 months versus 12.1 months, hazard ratio (HR) 0.81; 95% confidence interval (CI) 0.71–0.93; P = 0.0018] [7].
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 | 0.056 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.035 | 0.033 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".