Clinical Use of Epidermal Growth Factor Receptor Testing in Patients With Advanced Lung Cancer by Physicians: Survey of US and International Patterns
Bibliographic record
Abstract
PURPOSE: Guidelines recommend testing for EGFR mutation at diagnosis of advanced non-small-cell lung cancer to guide treatment. Two surveys, 18 months apart, aimed to identify changes in EGFR mutation testing and treatment practices in non-small-cell lung cancer. METHODS: The first survey of 562 physicians from Canada, France, Germany, Italy, Japan, South Korea, Spain, Taiwan, the United Kingdom, and the United States was conducted between December 2014 and January 2015. The second, between July and August 2016, surveyed 707 physicians in the same countries with the addition of China; China was excluded from year-on-year comparisons. RESULTS: Globally (excluding China), physicians requested EGFR mutation testing in 80% (excluding China; 2015: 81%) of patients before first-line therapy. In 2016, 18% of results were not received before initiating treatment, a significant improvement over 2015 (23%). Reasons for not testing included tumor histology, insufficient tissue, poor performance status, and long turnaround time, although this had significantly improved in 2016 from 2015. Prolonging of survival/extending life was deemed the most important therapy goal in first-line treatment of both cohorts. CONCLUSION: Improvements in availability of test results before first-line therapy were seen, but incomplete implementation of guidelines is still observed, resulting in a large proportion of patients not receiving tyrosine kinase inhibitor treatment on the basis of mutation status. The reasons for not testing remained the same, year-on-year: tumor histology, insufficient tissue, poor performance status, and long test turnaround time. Receiving timely results must be addressed, if treatment parity for eligible patients can be achieved. Physician education and closer guideline concordance are key steps to improve outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".