Defining the Criteria for Reflex Testing for BRAF Mutations in Cutaneous Melanoma Patients
Bibliographic record
Abstract
Targeted therapy has been developed through an in-depth understanding of molecular pathways involved in the pathogenesis of melanoma. Approximately ~50% of patients with melanoma have tumors that harbor a mutation of the BRAF oncogene. Certain clinical features have been identified in BRAF-mutated melanomas (primary lesions located on the trunk, diagnosed in patients <50, visibly pigmented tumors and, at times, with ulceration or specific dermatoscopic features). While BRAF mutation testing is recommended for stage III–IV melanoma, guidelines differ in recommending mutation testing in stage II melanoma patients. To fully benefit from these treatment options and avoid delays in therapy initiation, advanced melanoma patients harboring a BRAF mutation must be identified accurately and quickly. To achieve this, clear definition and implementation of BRAF reflex testing criteria/methods in melanoma should be established so that patients with advanced melanoma can arrive to their first medical oncology appointment with a known biomarker status. Reflex testing has proven effective for a variety of cancers in selecting therapies and driving other medical decisions. We overview the pathophysiology, clinical presentation of BRAF-mutated melanoma, current guidelines, and present recommendations on BRAF mutation testing. We propose that reflex BRAF testing should be performed for every melanoma patient with stages ≥IIB.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".