MétaCan
Menu
Back to cohort
Record W4388977723 · doi:10.1097/moo.0000000000000945

Where do we stand with immunotherapy for nonmelanoma skin cancers in the curative setting?

2023· article· en· W4388977723 on OpenAlexaff
Andrea Alberti, Cristina Gurizzan, Alice Baggi, Paolo Bossi

Bibliographic record

VenueCurrent Opinion in Otolaryngology & Head & Neck Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsImmunotherapyBasal cell carcinomaMedicineSkin cancerClinical trialOncologyMerkel cell carcinomaDermatologyImmune checkpointImmune systemCancer researchInternal medicineBasal cellImmunologyCarcinomaCancer

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Nonmelanoma skin cancers (NMSC) represent a heterogeneous group of diseases that encompasses among the principal histologies basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and Merkel cell carcinoma (MCC). Given the fact that high tumor mutational burden due to ultraviolet mutagenesis represents a common hallmark of NMSCs, immunotherapy has proved to be a promising therapeutic approach in recent years. The aim of this review is to shed light on immunotherapy applications in NMSCs in the curative setting. RECENT FINDINGS: Immune checkpoint inhibitors represent the first-line treatment of choice for advanced cSCC and MCC, while in second line for BCC. Given this success, more and more trials are evaluating the use of immune checkpoint blockade in neoadjuvant setting for NMSCs. Clinical trials are still ongoing, with the most mature data being found in cSCC. Also, translational studies have identified promising biomarkers of response. SUMMARY: Locoregional treatments of NMSCs can have non negligible functional and cosmetic impacts on patients, affecting their quality of life. As immunogenic diseases, neoadjuvant immunotherapy represents a promising treatment that could change the therapeutic path of these patients. Upcoming results from clinical trials will address these crucial issues.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.220
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.346
Teacher spread0.308 · 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.

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Otolaryngology & Head & Neck SurgerySame topicPolyomavirus and related diseasesFrench-language works237,207