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Record W4280529326 · doi:10.1200/edbk_349799

Checkpoint Immunotherapy in Pediatrics: Here, Gone, and Back Again

2022· article· en· W4280529326 on OpenAlexaff
Adrienne H. Long, Daniel A. Morgenstern, Amaury Leruste, Franck Bourdeaut, Kara L. Davis

Bibliographic record

VenueAmerican Society of Clinical Oncology Educational Book · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineImmunogenicityCancerImmunotherapyPediatric oncologyClinical trialLymphomaPediatric cancerOncologyImmune systemInternal medicineImmunologyPediatrics

Abstract

fetched live from OpenAlex

The role of immune checkpoint inhibitors (ICIs) in the treatment of pediatric cancers continues to evolve. Such therapies function by augmenting existing antitumor T-cell responses that have been rendered ineffective by inhibitory pathways. Although ICIs have proven highly effective for adult cancers, initial phase I/II clinical trials using single-agent ICIs against unselected pediatric cancers have been overall disappointing. With the exception of pediatric classic Hodgkin lymphoma, responses to ICIs have been infrequent, likely stemming from an inherent difference in the immunogenicity of childhood cancers, which, on average, have far fewer neoantigens than adult cancers. Recently, however, hope has reemerged that certain subsets of children with cancer may benefit from ICI therapies. In preliminary studies, patients with both pediatric hypermutated and SMARCB1-deficient cancers have had impressive responses to ICI therapies, likely as a result of underlying biologies that enhance neoantigen expression and tumoral inflammation. Dedicated trials are ongoing to fully evaluate the efficacy of ICIs for patients with these subsets of pediatric 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.417
Teacher spread0.370 · 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 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

Citations55
Published2022
Admission routes1
Has abstractyes

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