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 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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".