The Role of the Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors as Therapy for Advanced, Metastatic, and Recurrent Non-Small-Cell Lung Cancer: A Canadian National Consensus Statement
Bibliographic record
Abstract
PURPOSE: To provide consensus recommendations on the use of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIS) in patients with advanced or meta-static non-small-cell lung cancer (NSCLC). METHODS: Using a systematic literature search, phase II trials, randomized phase III trials, and meta-analyses were identified for inclusion. RESULTS: A total of forty-six trials were included. Clear evidence is available that EGFR-TKIS should not be administered concurrently with platinum-based chemotherapy as first-line therapy in advanced or metastatic nsclc. Evidence is currently insufficient to recommend single-agent EGFR-TKIS as first-line therapy either in unselected populations or in populations selected on the basis of molecular or clinical characteristics. Following failure of platinum-based chemotherapy, the evidence suggests that second-line EGFR-TKIS or second-line chemotherapy result in similar survival. Quality of life and symptom improvement for patients treated with an EGFR-TKI appear better than they do for patients treated with second-line docetaxel. Sequence of therapy may not appear to be important, but if survival is the outcome of interest, the goal should be to optimize the number of patients receiving three lines of therapy. Based on available data, molecular markers and clinical characteristics do not appear to be predictive of a differential survival benefit from an EGFR-TKI and therefore those factors should not be used to select patients for EGFR-TKI therapy. CONCLUSIONS: The EGFR-TKIS represent an additional therapy in the treatment of advanced or metastatic NSCLC. The results of ongoing clinical trials may define the optimal role for these agents and the effectiveness of combinations of these agents with other targeted agents.
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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 | 0.093 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".