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Record W4307264302 · doi:10.3389/ti.2022.10915

ESOT Consensus Platform for Organ Transplantation: Setting the Stage for a Rigorous, Regularly Updated Development Process

2022· review· en· W4307264302 on OpenAlexaff
Umberto Cillo, Annemarie Weißenbacher, Liset Pengel, Ina Jochmans, Daniele Roppolo, Cristiano Amarelli, L Belli, Marina Berenguer, Aiko P. J. de Vries, Joana Ferrer, John J. Friedewald, Lucrezia Furian, Sharlene A. Greenwood, Diethard Monbaliu, Silvio Nadalin, Arne Neyrinck, Mario Strazzabosco, Christian Toso, Gianluigi Zaza, Raj Thuraisingham, Thierry Berney, Luciano Potena, Núria Montserrat, Nazia Selzner

Bibliographic record

VenueTransplant International · 2022
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConsensus conferenceMedicineTransplantationGuidelineEvidence-based medicineExpert opinionMEDLINEMedical educationIntensive care medicineAlternative medicinePathologySurgeryPolitical scienceLaw

Abstract

fetched live from OpenAlex

The European Society for Organ Transplantation (ESOT) has created a platform for the development of rigorous and regularly updated evidence based guidelines for clinical practice in the transplantation field. A dedicated Guideline Taskforce, including ESOT-council members, a representative from the Centre for Evidence in Transplantation, editors of the journal Transplant International has developed transparent procedures to guide the development of guidelines, recommendations, and consensus statements. During ESOT's first Consensus Conference in November 2022, leading experts will present in-depth evidence based reviews of nine themes and will propose recommendations aimed at reaching a consensus after public discussion and assessment by an independent jury. All recommendations and consensus statements produced for the nine selected topics will be published including the entire evidence-based consensus-finding process. An extensive literature review of each topic was conducted to provide final evidence and/or expert opinion.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.377
Teacher spread0.291 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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