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Record W4385241321 · doi:10.3390/cancers15153754

Efficacy and Safety of Rechallenge with BRAF/MEK Inhibitors in Advanced Melanoma Patients: A Systematic Review and Meta-Analysis

2023· review· en· W4385241321 on OpenAlexaff
Jonathan N. Priantti, Maysa Vilbert, Thiago Madeira, Francisco Cézar Aquino de Moraes, Erica C. Koch Hein, Anwaar Saeed, Ludimila Cavalcante

Bibliographic record

VenueCancers · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineMelanomaOncologyInternal medicineOverall survivalMeta-analysisRefractory (planetary science)ImmunotherapyDiseaseClinical PracticeCancer researchCancerPhysical therapy

Abstract

fetched live from OpenAlex

This systematic review and meta-analysis aims to evaluate the efficacy and safety of rechallenging advanced melanoma patients with BRAFi/MEKi. Seven studies, accounting for 400 patients, were included. Most patients received immunotherapy before the rechallenge, and 79% underwent rechallenge with the combination of BRAFi/MEKi. We found a median progression-free survival of 5 months and overall survival of 9.8 months. The one-year survival rate was 42.63%. Regarding response, ORR was 34% and DCR 65%. There were no new or unexpected safety concerns. Rechallenge with BRAFi/MEKi can improve outcomes in advanced melanoma patients with refractory disease. These findings have significant implications for clinical practice, particularly in the setting of progressive disease in later lines and limited treatment options.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.021
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.046
GPT teacher head0.306
Teacher spread0.260 · 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 designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations29
Published2023
Admission routes1
Has abstractyes

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