MétaCan
Menu
Back to cohort
Record W2596378384 · doi:10.1097/mpg.0000000000001564

Early and Late Factors Impacting Patient and Graft Outcome in Pediatric Liver Transplantation

2017· article· en· W2596378384 on OpenAlexaff
Valérie A. McLin, Upton Allen, Olivia Boyer, John C. Bucuvalas, M. Colledan, María Cristina Cuturi, Lorenzo D’Antiga, Dominique Debray, Antal Dezsőfi, Jean de Ville de Goyet, Anil Dhawan, Özlem Durmaz, Christine S. Falk, Sandy Feng, Björn Fischler, Stéphanie Franchi‐Abella, E. Frauca, Rainer Ganschow, Stephen Gottschalk, Nedim Hadžić, Loreto Hierro, Simon Horslen, Stefan G. Hübscher, Vincent Karam, Déirdre Kelly, Britta Maecker‐Kolhoff, George Mazariegos, Patrick McKiernan, Anette Melk, Valério Nobili, Funda Özgenç, Raymond Reding, Marco Sciveres, Khalid Sharif, Piotr Socha, Christian Toso, Pietro Vajro, Anita Verma, Barbara E. Wildhaber, Ulrich Baumann

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2017
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersAstellas PharmaUnited European GastroenterologyBundesministerium für Bildung und ForschungNorth American Society for Pediatric Gastroenterology, Hepatology and Nutrition
KeywordsMedicineLiver transplantationOutcome (game theory)TransplantationIntensive care medicinePediatricsSurgery

Abstract

fetched live from OpenAlex

As pediatric liver transplantation comes of age, experts gathered to discuss current paradigms and define gaps in knowledge warranting research to further improve patient and graft outcomes. Identified areas ripe for collaborative research include understanding the molecular and cellular mechanisms of tolerance and the role of donor-specific antibodies, considering ways to expand donor pool, minimizing long-term side effects of immunosuppression, and fine-tuning surgical techniques to minimize biliary and vascular complications.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.353

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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 designObservational
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

Citations23
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Pediatric Gastroenterology and NutritionSame topicOrgan Transplantation Techniques and OutcomesFrench-language works237,207