Therapeutic Landscape of Metastatic Non-Small-Cell Lung Cancer in Canada in 2020
Bibliographic record
Abstract
Lung cancer is the most commonly diagnosed cancer in Canada and remains associated with high mortality. Nevertheless, recent advances in the fields of immuno-oncology and precision medicine have led to significant improvements in clinical outcome in metastatic non-small-cell lung cancer (nsclc). Those improvements were facilitated by a greater understanding of the biologic classification of nsclc, which catalyzed discoveries of novel therapies. Here, we present a comprehensive review of the recent avalanche of practice-changing trials in metastatic nsclc, and we offer an approach to the management of this disease from a Canadian perspective. We begin with an overview of the pathologic and molecular characterization of metastatic nsclc. Next, we review the indications for currently approved immune checkpoint inhibitors, and we provide an approach to the management of disease with a driver mutation. Finally, we address future avenues in both diagnostics and therapeutics for patients with advanced and metastatic 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".