Turnaround Times in Melanoma BRAF Testing and the Impact on the Initiation of Systemic Therapy at a Single Tertiary Care Cancer Center
Bibliographic record
Abstract
PURPOSE: The identification of BRAF mutations in melanoma enables targeted therapy and improves patient outcomes. Barriers to BRAF molecular testing affect the quality of care and therapeutic options. METHODS: This retrospective study mapped BRAF testing timelines in adult patients with melanoma at the Princess Margaret Cancer Centre to identify obstacles to timely BRAF reporting and its impact on the initiation of therapy. RESULTS: Sixty-six cases were included. The median time between BRAF request and result was 12 days (95% CI, 8 to 15) when the BRAF test was ordered by pathology, compared with 20 days (95% CI, 16 to 23) if the test was requested by another specialist ( P < .001). When the BRAF test and biopsy were performed within the same institution, the BRAF median turnaround time (TAT) was 13 days (95% CI, 6 to 19) compared with 19 days (95% CI, 16 to 21) if the sample was transferred from another institution ( P = .02). Forty-seven patients received systemic therapy, and 20 had metastatic disease. In the metastatic subgroup, if the BRAF result was available at the first medical oncology visit, the initiation of treatment was 20 days (95% CI, 9.6 to 30.3), but was delayed to 31 days (95% CI, 10.8 to 51.1) if the BRAF result was not available ( P = .03). CONCLUSION: This study showed variations in BRAF test results in TAT. One factor affecting this timeline is the transfer time, which can be streamlined by pathology reflex testing. Delays in TAT affect the timing and type of therapeutic intervention, especially in patients with stage IV disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".