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BRAF testing timelines and impact on the starting of systemic treatment.

2021· article· en· W3168328251 on OpenAlexaff
Diana P. Arteaga, Zaid Saeed Kamil, Thiago Pimentel Muniz, Diane Liu, Ian King, Tracy Stockley, Diana Gray, Samuel D. Saibil, David Hogg, Anna Spreafico, Marcus O. Butler

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineVemurafenibMelanomaStage (stratigraphy)Internal medicineOncologyTurnaround timeCancerTimelineRetrospective cohort studyDiseaseLog-rank testMetastatic melanomaSurvival analysisCancer research

Abstract

fetched live from OpenAlex

e21575 Background: Targeted therapy with BRAF and MEK inhibitors constitute part of the standard treatment for BRAF V600 mutated melanoma. Timely detection of BRAF mutation is necessary for clinicians and patients to make treatment decisions. We aimed to map the BRAF testing timelines from the time of request until the reported result in order to assess obstacles to timely BRAF reporting in our community, and its impact on the initiation of systemic therapy. Methods: In this single-center retrospective study, we included adult patients referred to the Medical Oncology Department at the Princess Margaret Cancer Centre (PM) from January 2019 to August 2019 with histologically confirmed cutaneous or mucosal melanoma and BRAF molecular testing performed in 2019. The Log-Rank method was applied to detect differences in BRAF turnaround time and time to treatment initiation in specified subgroups. A p value < 0.05 was considered statistically significant. Results: Sixty-six cases were identified. The median age was 64 (24-88), and 42 (64%) were male. At the time of BRAF request, 10 (15%) patients had stage II, 33 (50%) had stage III, and 23 (35%) had stage IV disease. Twenty-eight (43%) were positive for the BRAF V600E/K mutation by ARMS assay and 4 (6%) for other variants by NGS test. Thirty-three (50%) patients had the BRAF test available at their first PM Medical Oncology visit. Median time between BRAF request and result was 17 days; when a reflex BRAF test was ordered by the Pathology Department, the median turnaround time was 12 days (95% CI 8-15), compared to 20 days (95% CI 16-23) if the order was requested by another specialist ( p < 0.001). Median time to transfer samples between institutions was 6 days. If the BRAF test was processed within the institution where the biopsy was performed, the BRAF median turnaround time was 13 days (95% CI 6-19) compared to 19 days (95% CI 16-21) if a sample was transferred to another institution ( p = 0.02). In total, 49 patients had systemic therapy. Median time between the first visit with Medical Oncology and treatment initiation was 29 days and was not statistically different if the BRAF result was available or not (28 vs. 34 days; p = 0.09). In the subgroup with stage IV disease (Table), 20 patients received systemic therapy; the median time to treatment initiation was 24 days and differed with BRAF result availability (20 vs. 31 days, p = 0.03). Conclusions: The current BRAF testing timeline at the PM varies from days to weeks. A major factor impacting this timeline is transfer time, which can be streamlined by pathology reflex testing. Delays in turnaround time appears to impact subsequent timing and type of therapeutic interventions, especially in patients with stage IV disease.[Table: see text]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.153
GPT teacher head0.441
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
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