Evidence-Based Best Practices for EGFR T790M Testing in Lung Cancer in Canada
Bibliographic record
Abstract
Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIS) are recommended as first-line systemic therapy for patients with non-small-cell lung cancer (NSCLC) having mutations in the EGFR gene. Resistance to TKIS eventually occurs in all nsclc patients treated with such drugs. In patients with resistance to TKIS caused by the EGFR T790M mutation, the third-generation TKI osimertinib is now the standard of care. For optimal patient management, accurate EGFR T790M testing is required. A multidisciplinary working group of pathologists, laboratory medicine specialists, medical oncologists, a respirologist, and a thoracic radiologist from across Canada was convened to discuss best practices for EGFR T790M mutation testing in Canada. The group made recommendations in the areas of the testing algorithm and the pre-analytic, analytic, and post-analytic aspects of clinical testing for both tissue testing and liquid biopsy circulating tumour DNA testing. The recommendations aim to improve EGFR T790M testing in Canada and to thereby improve 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 | 0.040 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".