Real-World Evidence Study of Patients with KRAS-Mutated NSCLC in Finland
Bibliographic record
Abstract
While KRAS is the most frequently mutated oncogene in non-small cell lung cancer (NSCLC), KRAS-mutant tumors have long been considered difficult to treat and thus, an unmet need still remains. Partly due to the lack of targeted treatments, comprehensive real-world description of NSCLC patients with KRAS mutation is still largely missing in Finland. In this study, all adult patients diagnosed with locally advanced and unresectable or metastatic NSCLC from 1 January 2018 to 31 August 2020 at the Hospital District of Helsinki and Uusimaa were first identified in this retrospective registry-based real-world study. The final cohort included only patients tested with next generation sequencing (NGS) and was stratified by the KRAS mutation status. A total of 383 patients with locally advanced and unresectable or metastatic NSCLC and with NGS testing performed were identified. Patients with KRAS mutation (KRAS G12C n = 35, other KRAS n = 74) were younger than patients without KRAS mutations, were all previous or current smokers, and had more often metastatic disease at diagnosis. Also, these patients had poorer survival, with higher age, Charlson comorbidity index (CCI) being 5 or above, and KRAS G12C being the most significant risk factors associated with poorer survival. This suggests that the patients with KRAS mutation have a more aggressive disease and/or tumors with KRAS mutation are more difficult to treat, at least without effective targeted therapies.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".