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Record W4361008991 · doi:10.3390/cancers15071959

Novel Immunotherapeutic Approaches to Treating HPV-Related Head and Neck Cancer

2023· review· en· W4361008991 on OpenAlexaff
Nabil F. Saba, Saagar Pamulapati, Bhamini Patel, Mayur D. Mody, Primož Strojan, Robert P. Takes, Antti Mäkitie, Oded Cohen, Pia Pace‐Asciak, Jan B. Vermorken, Carol R. Bradford, Arlene A. Forastiere, Yong Teng, Andreas Wieland, Alfio Ferlito

Bibliographic record

VenueCancers · 2023
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineImmunotherapyMalignancyOncologyHead and neck cancerHead and neck squamous-cell carcinomaDiseaseIncidence (geometry)CancerImmune systemInternal medicineCancer immunotherapyTumor microenvironmentImmunology

Abstract

fetched live from OpenAlex

Head and neck cancer (HNC) is the seventh most common malignancy, with oropharyngeal squamous cell carcinoma (OPSCC) accounting for a majority of cases in the western world. While HNC accounts for only 5% of all cancers in the United States, the incidence of a subset of OPSCC caused by human papillomavirus (HPV) is increasing rapidly. The treatment for OPSCC is multifaceted, with a recently emerging focus on immunotherapeutic approaches. With the increased incidence of HPV-related OPSCC and the approval of immunotherapy in the management of recurrent and metastatic HNC, there has been rising interest in exploring the role of immunotherapy in the treatment of HPV-related OPSCC specifically. The immune microenvironment in HPV-related disease is distinct from that in HPV-negative OPSCC, which has prompted further research into various immunotherapeutics. This review focuses on HPV-related OPSCC, its immune characteristics, and current challenges and future opportunities for immunotherapeutic applications in this virus-driven cancer.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.289
GPT teacher head0.396
Teacher spread0.106 · 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 designOther design
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

Citations25
Published2023
Admission routes1
Has abstractyes

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