Actionable Mutations and Survival Rates in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Background: In Jordan, lung cancer ranks as the second most common tumor, and there is an urgent need to explore the genetic landscape of lung cancer. This study aimed to identify the actionable mutations in lung cancer samples in Jordanians by targeted next-generation sequencing (NGS) and to investigate the correlations with clinical and pathological parameters. Methods: Totally, 121samples were prepared for NGS by DNA extractions from formalin-fixed paraffin-embedded (FFPE) blocks, followed by library preparation using the AmpliSeq Colon and Lung panel, which covers mutational hot spot regions for 22 cancer genes. Results: ). There are no significant differences between actionable mutation carriers and non-carriers concerning histological tumor type, tumor stage, metastasis, smoking habits, and gender. However, the analysis of survival probabilities revealed lower survival times for females compared to males, as well as for those patients who had metastasis events, smoking, or relapse after treatment. Conclusions: The type and rates of mutations detected for lung tumors in Jordan are relatively similar to those found in other populations previously studied, although some differences exist. However, lung tumors in Jordan require new customized treatment prescriptions based on prior genetic studies, as part of the hoped-for trend toward precision medicine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".