Biomarker Testing and Patterns of Treatment in Patients with NSCLC: An International Association for The Study of Lung Cancer Analysis of American Society of Clinical Oncology CancerLinQ Discovery Data
Bibliographic record
Abstract
Introduction: Precision medicine has resulted in improved outcomes for non small cell lung cancer (NSCLC), whereas biomarker testing is considered critical for guiding treatment decisions for advanced-stage NSCLC, and adoption of testing in routine practice is variable. We studied the utilization of biomarker testing in advanced NSCLC. Methods: The American Society of Clinical Oncology (ASCO) CancerLinQ Discovery data set was queried to identify patients diagnosed with lung cancer between 2010 and 2018. Data on demographics, tumor stage, histology, and treatments were extracted, and receipt of biomarker testing was investigated as the primary outcome. Univariate association of each clinicopathological variable with the biomarker testing outcome was performed using a chi-square test for categorical variables and an analysis of variance test for numerical variables. A multivariable logistic regression analysis with backward selection at an alpha of 0.05 was reported. All analyses were conducted using SAS 9.4. Results: < 0.001) were associated with a significantly higher likelihood of having biomarker testing. These results were also confirmed in a subgroup analysis of patients with adenocarcinoma. Conclusion: In this analysis of a United States-based real-world data set of patients with stage IV NSCLC, the Asian race and female sex were associated with a higher likelihood of having biomarker testing performed. The overall percentage of patients undergoing testing remained suboptimal.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".