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Record W7112447226

Clinical parameters associated with immunotherapy outcomes in metastatic cutaneous squamous cell carcinoma

2023· article· en· W7112447226 on OpenAlexaff

Bibliographic record

VenueResearch Explorer (The University of Manchester) · 2023
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsImmunotherapyCohortOverall survivalHead and neckHead and neck cancerHead and neck squamous-cell carcinomaBasal cellCarcinoma
DOInot available

Abstract

fetched live from OpenAlex

Lay abstract: We want to understand which subgroups of patients we treat respond well to immunotherapies. The use of anti-programmed death 1 (PD1) therapy cemiplimab has shown significant clinical benefit in patients with recurrent/ metastatic cutaneous squamous cell carcinoma (R/M-cSCC), with durable responses seen in most patients. However, around 30% of patients do not benefit from the treatment, and there is a need to develop predictors of response and resistance to therapy. In this study we aimed to identify clinical parameters that predict response/resistance to therapy in a cohort of 88 R/M-cSCC patients treated with cemiplimab at three UK cancer centers. The results showed that patients with R/M-cSCC with a head and neck primary site had a significantly improved overall survival (OS) and progression-free survival (PFS) compared with other primary sites when treated with cemiplimab. The median OS and PFS were not reached for the entire cohort, and the 2-year OS rate was 60%. The overall response rate (ORR) was 60%, and 40% of patients developed immune-related toxicities of any CTCAE grade, while 13% developed grade 3 or above. The study concludes that cemiplimab treatment demonstrated significant clinical benefit with a manageable side effect profile, and future work should investigate the observed difference in survival outcomes based on primary site further.

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 score0.007
Threshold uncertainty score0.518

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 studies0.0000.001
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 head0.130
GPT teacher head0.355
Teacher spread0.225 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueResearch Explorer (The University of Manchester)Same topicNonmelanoma Skin Cancer StudiesFrench-language works237,207