Rate of Egfr Mutation Testing for Patients with Nonsquamous Non-Small-Cell Lung Cancer with Implementation of Reflex Testing by Pathologists
Bibliographic record
Abstract
Background: Testing for mutation of the EGFR (epidermal growth factor receptor) gene is a standard of care for patients with advanced nonsquamous non-small-cell lung cancer (nsclc). To improve timely access to EGFR results, a few centres implemented reflex testing, defined as a request for EGFR testing by the pathologist at the time of a nonsquamous nsclc diagnosis. We evaluated the impact of reflex testing on EGFR testing rates. Methods: A retrospective observational review of the Web-based AstraZeneca Canada EGFR Database from 1 April 2010 to 31 March 2014 found centres within Ontario that had requested EGFR testing through the database and that had implemented reflex testing (with at least 2 years’ worth of data, including the pre- and post-implementation period). Results: The 7 included centres had requested EGFR tests for 2214 patients. The proportion of pathologists requesting EGFR tests increased after implementation of reflex testing (53% vs. 4%); conversely, the proportion of medical oncologists requesting tests decreased (46% vs. 95%, p < 0.001). After implementation of reflex testing, the mean number of patients having EGFR testing per centre per month increased significantly [12.6 vs. 4.9 (range: 4.5–14.9), p < 0.001]. Before reflex testing, EGFR testing rates showed a significant monthly increase over time (1.37 more tests per month; 95% confidence interval: 1.19 to 1.55 tests; p < 0.001). That trend could not account for the observed increase with reflex testing, because an immediate increase in EGFR test requests was observed with the introduction of reflex testing (p = 0.003), and the overall trend was sustained throughout the post–reflex testing period (p < 0.001). Conclusions: Reflex EGFR testing for patients with nonsquamous nsclc was successfully implemented at multiple centres and was associated with an increase in EGFR testing.
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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 | 0.000 | 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 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".