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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".