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Record W3027155039 · doi:10.3390/cancers12051321

Insights into the Molecular Mechanisms Behind Intralesional Immunotherapies for Advanced Melanoma

2020· review· en· W3027155039 on OpenAlexaff
Dejan Vidovic, Carman A. Giacomantonio

Bibliographic record

VenueCancers · 2020
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMelanomaMedicineImmune systemMetastatic melanomaCancerDiseaseImmunotherapySkin cancerAdverse effectImmune surveillanceMetastasisImmunologyCancer researchDermatologyInternal medicine

Abstract

fetched live from OpenAlex

The incidence of cutaneous melanoma, a highly malignant skin cancer, is increasing yearly. While surgical removal of the tumor is the mainstay of treatment for patients with locally confined disease, those with metastases face uncertainty when it comes to their treatment. As melanoma is a relatively immunogenic cancer, current guidelines suggest using immunotherapies that can rewire the host immune response to target melanoma tumor cells. Intralesional therapy, where immunomodulatory agents are injected directly into the tumor, are an emerging aspect of treatment for in-transit melanoma because of their ability to mitigate severe off-target immune-related adverse events. However, their immunomodulatory mechanisms are poorly understood. In this review, we will summarize and discuss the different intralesional therapies for metastatic melanoma with respect to their clinical outcomes and immune molecular mechanisms.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.354
Teacher spread0.319 · 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 teacher head, not a consensus.

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

Citations15
Published2020
Admission routes1
Has abstractyes

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