MétaCan
Menu
← Back to cohort
Record W4377000813 · doi:10.2139/ssrn.4449379

Banff 2022 Pancreas Transplantation Multidisciplinary Report: Refinement of Guidelines for TCMR, AMR, Islet and Non-Rejection Pathologies. Assessment of Duodenal Cuff Biopsies and Non-Invasive Diagnostic Methods

2023· article· en· W4377000813 on OpenAlexaff
Cinthia B. Drachenberg, Maike Büttner‐Herold, Pedro Ventura‐Aguiar, Catherine Horsfield, Alexei Mikhailov, John C. Papadimitriou, Surya V. Seshan, Marcelo Perosa, Ugo Boggi, Pablo Uva, Michael R. Rickels, Krzysztof Grzyb, Lois J. Arend, Míriam Cuatrecasas, María Fernanda Toniolo, Alton B. Farris, Karine Renaudin-Autain, Lizhi Zhang, Candice Roufosse, Angelika C. Gruessner, Rainer W.G. Gruessner, Raja Kandaswamy, Steven White, George W. Burke, Diego Cantarovich, Ronald F. Parsons, Matthew Cooper, Yogish C. Kudva, Aleksandra Kukla, Abdolreza Haririan, Sandesh Parajuli, Juan Francisco Merino-Torres, María Argente, Raphaël Meier, Ty B. Dunn, Richard Ugarte, Joseph Sushil Rao, Fabio Vistoli, Robert J. Stratta, Jon S. Odorico

Bibliographic record

VenueSSRN Electronic Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicinePancreasPancreas transplantationIsletTransplantationBasiliximabBiopsyGrading (engineering)ImmunosuppressionPathologyIntensive care medicineInternal medicineRadiologyKidney transplantationInsulin

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)0.003
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.040
GPT teacher head0.411
Teacher spread0.371 · 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
GenreMethods

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

Citations0
Published2023
Admission routes1
Has abstractno

Explore more

Same venueSSRN Electronic Journal→Same topicRenal Transplantation Outcomes and Treatments→French-language works237,207→