Impact of Reflex Testing for <i>BRAF</i> Mutational Status in Advanced Melanoma
Bibliographic record
Abstract
CONTEXT.—: The use of targeted therapy in patients with advanced, BRAF-mutated melanomas has necessitated timely access to BRAF mutational status in order for clinicians to proceed with treatment decisions. OBJECTIVE.—: To assess the impact of pathologist-initiated reflex BRAF testing in patients with advanced melanoma on laboratory turnaround time and time to systemic treatment. DESIGN.—: At our tertiary care center and 3 affiliated community hospitals, we implemented a guideline for pathologist-initiated reflex testing for BRAF mutational status in patients diagnosed with melanoma and positive lymph nodes or new diagnosis of a metastatic site. Retrospective review was performed for 65 cases of advanced melanoma for which BRAF testing was ordered, during a period inclusive of 6 months before and after guideline implementation. RESULTS.—: Implementation of reflex testing guidelines did not significantly affect the overall number of BRAF tests ordered for patients with melanoma. In cases with reflex testing compared to routine testing, total turnaround time was reduced by from 52.5 ± 5.6 to 18.6 ± 1.0 days (P < .001). In patients who received systemic therapy, without intentional delay by interval completion lymph node dissection (CLND), the use of reflex BRAF testing reduced time to systematic treatment from 71.7 ± 11.4 to 37.7 ± 4.6 days (P = .02). Time to systematic treatment was unchanged in those who underwent interval CLND (118.9 ± 10.9 versus 110.5 ± 22.5; P = .75). CONCLUSIONS.—: These data support a recommendation for pathologist-initiated reflex testing of BRAF mutational status in advanced melanoma as a standard practice in pathology laboratories.
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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 | 0.002 | 0.012 |
| 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 | 0.000 |
| 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".