Treatment Paradigms for Patients with Metastatic Non-Small-Cell Lung Cancer: First-, Second-, and Third-Line
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Metastatic non-small-cell lung cancer (nsclc) is the leading cause of cancer mortality in Canada. Although treatment outcomes in advanced disease remain modest, with paradigm shifts in the approach to treatment, they are steadily improving. Customizing treatment based on histology and molecular typing has become the standard of care. EGFR genotyping and pathology subtyping should be considered routine in new diagnoses of metastatic nsclc. Treatment options for those with somatic EGFR activating mutations include gefitinib until progression, followed by standard chemotherapy. For patients with wild-type EGFR, or in patients whose EGFR genotype is unknown, platinum-based chemotherapy remains the first-line standard, with single-agent chemotherapy as an option for older patients and those who are unfit for platinum-doublet therapy. Patients with nonsquamous histology may receive treatment regimens incorporating pemetrexed or bevacizumab. In patients with squamous cell carcinoma, the latter agents should be avoided because of concerns about enhanced toxicity or decreased efficacy. Second-line chemotherapy is offered to a selected subgroup of patients upon progression and may include pemetrexed in non-squamous histology and docetaxel or erlotinib (or both) in all histologies. Currently, only erlotinib is offered as a third-line option in unselected nsclc patients after failure of first- and second-line chemotherapy. Maintenance therapy is emerging as a new option for patients, as are targeted therapies for particular molecular subtypes of nsclc, such as crizotinib in tumours harbouring the EML4-ALK gene rearrangement.
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.
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) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it