MétaCan
Menu
Back to cohort

The Emerging Therapeutic Landscape of Advanced Melanoma

2018· review· en· W2784833105 on OpenAlexfundno aff
Vanessa Henriques, Teresa Martins, Wolfgang Link, Bibiana I. Ferreira

Bibliographic record

VenueCurrent Pharmaceutical Design · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsnot available
FundersOncolytics BiotechAmgen
KeywordsMedicineTherapeutic modalitiesMelanomaModalitiesIntensive care medicineCancerImmunotherapyTreatment modalitySkin cancerMetastatic melanomaInternal medicineCancer research

Abstract

fetched live from OpenAlex

Melanoma is the deadliest form of skin cancer being responsible for 80% of skin cancer deaths. Furthermore, the incidence of metastatic melanoma has increased over the past three decades with a mortality rate that continues to rise faster than most of all other cancers. The last few years have witnessed an unparalleled change in treatment options for patients with metastatic melanoma by the development of new therapeutic strategies like targeted therapies and immunotherapies that highly improved the patient's prognosis. Despite the paradigm- shifting success of these novel treatments, their effectiveness is still limited by intrinsic or acquired resistance. The objective of this review is to provide an overview of the new available treatment modalities, criteria to select patients who might benefit from a specific therapy, mechanisms of innate and acquired resistance to these treatments and to discuss strategies to overcome drug resistance.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.121
GPT teacher head0.406
Teacher spread0.285 · 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 designNot applicable
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

Citations20
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Pharmaceutical DesignSame topicMelanoma and MAPK PathwaysFrench-language works237,207