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Record W3135316324 · doi:10.1016/j.micinf.2021.104804

Therapeutic interventions and mechanisms associated with gut microbiota-mediated modulation of immune checkpoint inhibitor responses

2021· review· en· W3135316324 on OpenAlexafffund
Matthew Wong, Philip Barbulescu, Bryan Coburn, Elaine Reguera-Nuñez

Bibliographic record

VenueMicrobes and Infection · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of Toronto
FundersPrincess Margaret Cancer Foundation
KeywordsMicrobiomeBiologyImmune systemGut microbiomeGut floraImmunologyImmunotherapyPsychological interventionImmune checkpointComputational biologyBioinformaticsMedicine

Abstract

fetched live from OpenAlex

The link between the gut microbiome and responsiveness to immune checkpoint inhibitor (ICI) therapy is now well established. New therapeutic opportunities exploiting this relationship are being developed with the goal of augmenting ICI efficacy. In this review, we summarize the foundational research establishing these interactions and discuss the mechanisms and novel therapeutic options associated with this gut microbiome-ICI connection.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.309
Teacher spread0.280 · 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 designBench or experimental
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

Citations9
Published2021
Admission routes2
Has abstractyes

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