Modern era systemic therapies: Expanding concepts of cure in early and locally advanced non‐small cell lung cancer
Bibliographic record
Abstract
Cure of cancer is a sensitive and multidimensional concept that is challenging to define, difficult to assert at the individual patient level, and often surrounded by controversy. The notion of cure in non-small cell lung cancer (NSCLC) has changed and continues to evolve with improvements in diagnosis and treatment. Targeted and immune therapies have recently entered the treatment landscape of stage I-III NSCLC. While some initial pivotal trials of such agents failed to improve survival, recently approved epidermal growth factor receptor (EGFR) inhibitors (in EGFR-mutated NSCLC) and immune checkpoint inhibitors have shown delays in disease recurrence or progression and unprecedented survival gains compared to previous standards of care. Additional data is now emerging supporting the benefit of treatment strategies based on alternation-matched targeting (anaplastic lymphoma kinase [ALK] inhibition in ALK-altered disease) and immune checkpoint inhibition in stage I-III NSCLC. Similar to previous developments in the treatment of early and locally advanced NSCLC, it is expected that statistically significant and clinically meaningful trial-level benefits will translate into real-world benefits, including improvements in cure measures. Parallel advances in molecular testing (e.g., circulating tumor DNA analyses) are also allowing for a deeper and more comprehensive characterization of disease status and treatment response. Given the impact that curative-intent treatments have on survival, it is critical that various stakeholders, including clinicians and patients, are aware of new opportunities to pursue cure in stage I-III NSCLC.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| 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; 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".