Evaluating the Implementation and Impact of BRAF Reflex Mutation Testing in Melanoma, Lung, and Colorectal Cancers
Bibliographic record
Abstract
BACKGROUND: Reflex molecular testing, the process of profiling specimens at diagnosis, is emerging in oncology, allowing for prompt therapy initiation, and potentially improving outcomes. This study evaluates the impact of reflex BRAF testing on treatment timelines and outcomes in melanoma, lung and colorectal cancers at the McGill University Health Center (MUHC). METHODS: A retrospective chart review was conducted at the MUHC. The study included patients with melanoma, lung, and colorectal cancers who underwent BRAF testing from 2017 to 2022. Data on demographics, cancer type, stage, test ordering specialist, testing method, and treatment outcomes were extracted. Statistical analyses included descriptive and multivariate regression analyses. RESULTS: 518 BRAF molecular tests were performed, with 173 patients meeting the inclusion criteria [median age 72 (IQR 60-80 years), 46.2% female]. Of these, 75.7% had melanoma, 23.7% colorectal cancer and 0.58% lung cancer. Pathologists ordered 71.1% of BRAF tests, primarily relying on immunohistochemistry. By 2022, all BRAF results were available before oncology consultations. Patients with pre-consultation BRAF results were more likely to receive targeted therapy (59.4% vs 36.4%). The availability of BRAF test results was associated with quicker treatment initiation and better alignment of therapy with mutation status. CONCLUSION: mutation testing, enhancing clinical care by ensuring the timely availability of test results. Delays in testing may adversely affect clinical decision-making and therapy selection, highlighting the need for standardized reflex testing protocols across Canada to optimize patient outcomes in melanoma, lung, and colorectal cancers.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".