Ultrarapid EGFR Testing in Non–Small Cell Lung Carcinoma Patients: Findings From a Canadian Clinical Testing Workflow
Bibliographic record
Abstract
Single vs multigene molecular testing modalities for lung cancer offer distinct advantages and risks. We examined lung cancer cases with clinically requested ultrarapid EGFR testing to (1) identify clinical features of rapid tested cases and their association with EGFR mutations, (2) evaluate performance of single-gene and multigene panel testing for ultrarapid tested patients, and (3) estimate laboratory costs and clinical outcomes. We include all retrospectively identified lung cancer patients who had ultrarapid Idylla EGFR testing during the study period. Demographic data were retrieved from clinical charts, and cost estimates were obtained from the BC Cancer Genetics and Genomics Laboratory. Of the 109 ultrarapid tests, 94 (86%) were technically successful, yielding a positive or negative result. Of these, 62 tests (66%) identified an EGFR mutation. Patients with negative or failed testing were offered panel sequencing (n = 47, 43%). Ultrarapid testing had a median 1-day turnaround time and 95% sensitivity for EGFR mutation detection relative to panel sequencing. East/Southeast Asian ethnicity and female sex were significantly associated with EGFR mutation positivity in a multivariate logistic regression model (P = .0001 and .029, respectively). The mean molecular testing cost per ultrarapid tested patient, including panel sequencing for cases with negative/failed rapid tests, was $550.53 (SD: $284), slightly less than the $571 cost for panel sequencing. Single-gene testing of patients with urgent clinical need or high probability of mutation may allow a rapid time to treatment at similar testing costs.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".