The Treatment of Metastatic Non-small Cell Lung Cancer (NSCLC) in New Era of Personalised Medicine
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer related mortality in Canada and USA. Majority of the patients present in advanced stage of the disease and of these only about 2% will be alive at 5 years. NSCLC is the most common form of lung cancer, accounting for approximately 87% of cases. Systemic chemotherapies have been used to treat metastatic NSCLC for decades, but the improvements of outcomes have reached a plateau. Recent advances in understanding signalling pathways for malignant cells, their interconections,the importance of various receptors and biomarkers and the interplay between various oncogenes have led to the development of targeted treatments that are improving both efficacy and safety of the treatments. Knowledge about the advantages of treatments with the targeted agents in metastatic NSCLC is growing rapidly. Combining various targeted agents or sequencing them properly will be important in the era of personalised medicine and overcoming development of the resistence to various targeted agents will be challenging. The importance of a team work,from the diagnosis through various treatments, to supportive care, from the interventional radiologists, pneumologists or surgeons, who have to obtain a satisfactory tumor tissue specimen, to pathologists, radiation and medical oncologists, to supportive care specialists, will be described in our publications. We will cover completely present and future approaches to personalised medicine in this rapidly evolving field of 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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.026 |
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".