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Record W3034869088 · doi:10.1111/ajt.16139

The Fourth International Workshop on Clinical Transplant Tolerance

2020· article· en· W3034869088 on OpenAlexaff
Fadi Issa, Samuel Strober, Joseph R. Leventhal, Tatsuo Kawai, Dixon B. Kaufman, Josh Levitsky, Megan Sykes, Valeria R. Mas, Kathryn J. Wood, Nancy D. Bridges, Sindhu Chandran, Joren C. Madsen, Peter Nickerson, Anthony J. Demetris, Fadi G. Lakkis, Angus W. Thomson

Bibliographic record

VenueAmerican Journal of Transplantation · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Allergy and Infectious DiseasesMedical Center, University of PittsburghMedical Research CouncilNational Institute for Health and Care ResearchWellcome Trust
KeywordsMedicineImmunosuppressionClinical trialIntensive care medicineTransplantationOrgan transplantationConsensus conferenceImmunologyInternal medicine

Abstract

fetched live from OpenAlex

The International Workshop on Clinical Transplant Tolerance is a biennial meeting that aims to provide an update on the progress of studies of immunosuppression minimization or withdrawal in solid organ transplantation. The Fourth International Workshop on Clinical Tolerance was held in Pittsburgh, Pennsylvania, September 5-6, 2019. This report is a summary of presentations on the status of clinical trials designed to minimize or withdraw immunosuppressive drugs in kidney, liver, and lung transplantation without subsequent evidence of rejection. All protocols had in common the use of donor or recipient cell therapy combined with organ transplantation. The workshop also included presentations of mechanistic studies designed to improve understanding of the cellular and molecular basis of tolerance and to identify potential predictors/biomarkers of tolerance. Strategies to enhance the safety of hematopoietic cell transplantation and to improve patient selection/risk stratification for clinical trials were also discussed.

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)0.017
metaresearch head score (Gemma)0.014
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: Other · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0100.005
Open science0.0030.009
Research integrity0.0060.014
Insufficient payload (model declined to judge)0.0610.030

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 head0.044
GPT teacher head0.359
Teacher spread0.315 · 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
GenreOther

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

Citations38
Published2020
Admission routes1
Has abstractyes

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