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Record W4383341159 · doi:10.1038/s41591-023-02453-x

Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial

2023· article· en· W4383341159 on OpenAlexafffund
Bertrand Routy, John Lenehan, Wilson H. Miller, Rahima Jamal, Meriem Messaoudene, Brendan A. Daisley, Cecilia Hes, Kait F. Al, Laura Martínez-Gili, Michal Punčochář, Scott Ernst, Diane Logan, Karl Bélanger, Khashayar Esfahani, Corentin Richard, Marina Ninkov, Gianmarco Piccinno, Federica Armanini, Federica Pinto, Mithunah Krishnamoorthy, René Figueredo, Paméla Thébault, Panteleimon G. Takis, Jamie Magrill, LeeAnn Ramsay, Lisa Derosa, Julian R. Marchesi, Seema Nair Parvathy, Arielle Elkrief, Ian R. Watson, Réjean Lapointe, Nicola Segata, S. M. Mansour Haeryfar, Benjamin H. Mullish, Michael Silverman, Jeremy P. Burton, Saman Maleki Vareki

Bibliographic record

VenueNature Medicine · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsOntario Institute for Cancer ResearchUniversité de MontréalSt Joseph's Health CareJewish General HospitalLawson Health Research InstituteWestern UniversityMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersMedical Research CouncilCanadian Institutes of Health ResearchAgence Nationale de la RechercheLotte and John Hecht Memorial Foundation
KeywordsPembrolizumabNivolumabMedicineTransplantationAdverse effectMicrobiomeClinical endpointMelanomaFecal bacteriotherapyInternal medicineImmune systemOncologyImmunologyImmunotherapyClinical trialClostridium difficileCancer researchBioinformaticsBiologyAntibioticsMicrobiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.337
Teacher spread0.329 · 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 designNon-randomized trial
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

Citations451
Published2023
Admission routes2
Has abstractno

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