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Record W4386851430 · doi:10.1016/j.gastha.2023.09.007

Colorectal Cancer Immunotherapy: State of the Art and Future Directions

2023· article· en· W4386851430 on OpenAlexaff
Alyssa Mauri Cornista, Maria Virginia Giolito, Kristi Baker, Hajar Hazime, Inès Dufait, Jashodeep Datta, Saratchandra Singh Khumukcham, Mark De Ridder, Jatin Roper, María T. Abreu, Karine Breckpot, Kevin Van der Jeught

Bibliographic record

VenueGastro Hep Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of Alberta
FundersNational Cancer InstituteNational Institutes of Health
KeywordsImmunotherapyMedicineColorectal cancerClinical trialCancer immunotherapyCancerOncologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Cancer immunotherapy has become an indispensable mode of treatment for a multitude of solid tumor cancers. Colorectal cancer (CRC) has been one of the many cancer types to benefit from immunotherapy, especially in advanced disease where standard treatment fails to prevent recurrence or results in poor survival. The efficacy of immunotherapy in CRC has not been without challenge, as early clinical trials observed dismal responses in unselected CRC patients treated with checkpoint inhibitors. Many studies and clinical trials have since refined immunotherapies available for CRC, solidifying immunotherapy as a powerful asset for CRC treatment. This review article examines CRC immunotherapies, from their foundation, through emerging avenues for improvement, to future directions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.007
GPT teacher head0.271
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations36
Published2023
Admission routes1
Has abstractyes

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