Impact of Reflex <i>EGFR</i>/<i>ALK</i> Testing on Time to Treatment of Patients With Advanced Nonsquamous Non–Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Optimal first-line systemic therapy for patients with advanced nonsquamous (nonsq) non-small-cell lung cancer (NSCLC) requires confirmation of EGFR/ ALK status, which can delay treatment. We evaluated the impact of reflex testing, defined as pathologists initiating EGFR/ ALK testing at the time of diagnosis of nonsq NSCLC, on time to treatment (TTT). METHODS: We conducted a retrospective review of patients with nonsq NSCLC with medical oncology consultation at Sunnybrook Odette Cancer Centre between March 18, 2010 and April 30, 2014. Data were compared during routine and reflex testing. TTT was defined as the interval between the first medical oncology visit with advanced NSCLC and the initiation of systemic therapy. RESULTS: A total of 306 patients were included (n = 232 for routine testing, n = 74 for reflex testing). There was a trend to improvement in median TTT with reflex testing (36 days [interquartile range {IQR}, 16 to 71 days v 26 days [IQR, 8 to 41 days], P = .071). Omitting patients with intentional delays in systemic therapy for low-volume disease, poor performance status, comorbidity management, and/or radiation therapy, median TTT improved (34 days [IQR, 15 to 67 days] v 22 days [IQR, 8 to 42 days], P = .049). Time to optimal first-line systemic therapy according to published guidelines improved (median, 36 days [IQR, 16 to 91 days] v 24 days [IQR, 8 to 43 days], P = .036). There was no impact on receipt of any first-line systemic therapy (55% v 59%, P = .66). The quality of biomarker testing improved, with fewer unsuccessful tests ( EGFR, 14% v 4%, P = .039; and ALK, 17% v 3%, P = .037). CONCLUSION: Reflex testing of EGFR/ ALK improved the time to optimal systemic therapy and the quality of biomarker testing for patients with advanced nonsq NSCLC.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".