The value of real-world evidence in supporting targeted therapies for patients with rare oncogenic drivers in mNSCLC
Bibliographic record
Abstract
With the ongoing discovery of various oncogenic driver mutations in metastatic non-small cell lung cancer (mNSCLC), a precision medicine approach has emerged, characterized by targeted therapies for select patient populations. Randomized controlled trials (RCT) remain the gold standard for evaluating efficacy and safety of such therapies; however, RCTs evaluating treatments for rare oncogenic drivers still face limitations, given small populations, potentially long-time horizon for outcome events to occur, and underrepresentation of certain subgroups. For these targeted therapies, the complementary nature between real-world evidence (RWE) and RCT may expand the totality of evidence available, to better inform treatment decision-making. In particular, treatments for rare oncogenic drivers can benefit from RWE that provides additional, generalizable clinical insights for subgroups underrepresented or ineligible for RCT, or confirms outcomes observed in RCT. As a discipline, RWE has seen significant advances in methodology and healthcare stakeholder acceptability, with potential for even greater innovation, and presents a valuable opportunity to support decision-making around access and use of targeted therapies for rare oncogenic drivers in mNSCLC.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".