Preparing for tomorrow: Molecular diagnostics and the changing nonsmall cell lung cancer landscape
Bibliographic record
Abstract
Over the last 10 to 15 years, the landscape of lung cancer has changed dramatically. Where cancers were previously described rather simplistically according to histological subtype, now molecular understanding of tumors has particularly resulted in segmentation of nonsmall cell lung cancer into many different subtypes. A multidisciplinary approach integrating a molecular testing algorithm that ideally includes reflex testing at diagnosis is recommended. This offers clinicians the opportunity to target treatment according to subtype. Identifying patients with rearrangements, such as those associated with the echinoderm microtubule-associated protein-like 4 (EML-4) anaplastic lymphoma kinase (ALK) fusion gene (the major focus of this paper) has allowed clinicians to tailor therapy to target these mutations. The challenge that faces clinicians treating lung cancer is how best to implement the science that sits behind these targeted therapies in clinical practice through the identification of appropriate patients. Precision medicine can lead to the choice of the right medicine for the right patients and is proving to be a better approach than treating unselected patients with systemic chemotherapy.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".