Challenges in Implementing Personalized Medicine for Lung Cancer within a National Healthcare System
Bibliographic record
Abstract
The traditional approach to the treatment of advanced non-small cell lung cancer (NSCLC) relied on the uniform use of cytotoxic chemotherapy. Over the last eight years, this paradigm of care has been shifting towards the use of molecularly targeted agents. Epidermal growth factor receptor (EGFR) mutations have emerged as an important biomarker for these targeted agents and multiple studies have shown that tyrosine kinase inhibitors (TKI) that inhibit EGFR are superior to traditional chemotherapy in patients possessing an EGFR mutation. Nationally funded health care systems face a number of challenges in implementing these targeted therapies, most related to the need to test for biomarkers that predict likelihood of benefiting from the drug. These obstacles include the challenge of getting a large enough tissue sample, workload of involved specialists, reliability of subtyping in NSCLC, differences in biomarker tests, and the disconnect between the funding of drugs and the related biomarker test. In order to improve patient outcomes, in a national healthcare system, there is a need for governments to accept the changing paradigm, invest in technology and build capacity for molecular testing to facilitate the implementation of improved patient care.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".