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Record W4283022888 · doi:10.1136/jitc-2022-004849

iRECIST and atypical patterns of response to immuno-oncology drugs

2022· review· en· W4283022888 on OpenAlexaff
Jorge Luis Ramón-Patiño, Sabine Schmid, Sally C. M. Lau, Lesley Seymour, Pierre-Olivier Gaudreau, Janice J.N. Li, Penelope Ann Bradbury, Emiliano Calvo

Bibliographic record

VenueJournal for ImmunoTherapy of Cancer · 2022
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreCanadian Partnership Against Cancer
Fundersnot available
KeywordsMedicineOncologyPrecision oncologyInternal medicineBioinformaticsCancerBiology

Abstract

fetched live from OpenAlex

With the advent of immunotherapy as one of the keystones of the treatment of our patients with cancer, a number of atypical patterns of response to these agents has been identified. These include pseudoprogression, where the tumor initially shows objective growth before decreasing in size, and hyperprogression, hypothesized to be a drug-induced acceleration of the tumor burden. Despite it being >10 years since the first immune-oncology drug was approved, neither the biology behind these paradoxical responses has been well understood, nor their incidence, identification criteria, predictive biomarkers, or clinical impact have been fully described. Immune-based Response Evaluation Criteria in Solid Tumors (iRECIST) guidelines have been published as a revision to the RECIST V.1.1 criteria for use in trials of immunotherapeutics, and the iRECIST subcommittee (of the RECIST Working Group) is working on elucidating these aspects, with data sharing a current major challenge to move forward with this unmet need in immuno-oncology.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.056
GPT teacher head0.430
Teacher spread0.375 · 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 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

Citations35
Published2022
Admission routes1
Has abstractyes

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