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Record W3004137799 · doi:10.1016/j.healun.2020.01.1345

ISHLT consensus statement on donor organ acceptability and management in pediatric heart transplantation

2020· editorial· en· W3004137799 on OpenAlexaff
Richard Kirk, Anne I. Dipchand, Ryan R. Davies, Oliver Miera, Gretchen B. Chapman, Jennifer Conway, Susan W. Denfield, Jeffrey G. Gossett, J. L. Johnson, Michael A. McCulloch, Martin Schweiger, Daniel Zimpfer, László Ablonczy, Iki Adachi, Dimpna C. Albert, Peta Alexander, Shahnawaz Amdani, Antonio Amodeo, Estela Azeka, Jean A. Ballweg, Gary Beasley, Jens Böhmer, Alison Butler, Manuela Camino, Javier Castro, Sharon Chen, M. Chrisant, U. Christen, Lara Danziger‐Isakov, Bibhuti B. Das, Melanie D. Everitt, Brian Feingold, Matthew J. Fenton, Luis Garcı́a-Guereta, Justin Godown, Dipankar Gupta, Claire Irving, Anna Joong, Mariska Kemna, S Khulbey, Steven J. Kindel, Kenneth R. Knecht, Ashwin K. Lal, Kimberly Y. Lin, Karen Lord, Thomas Møller, Deipanjan Nandi, Oliver Niesse, David M. Peng, Alicia Pérez‐Blanco, Ann R. Punnoose, Zdenka Reinhardt, David N. Rosenthal, Angie Scales, Janet Scheel, Renata Shih, Jonathan Smith, Jacqueline M. Smits, Josef Thul, Robert G. Weintraub, Steve Zangwill, Warren A. Zuckerman

Bibliographic record

VenueThe Journal of Heart and Lung Transplantation · 2020
Typeeditorial
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of AlbertaStollery Children's HospitalHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineIntensive care medicineStatement (logic)Waiting listHeart transplantsHeart transplantationTransplantationUSableSurgeryPolitical science

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)0.035
metaresearch head score (Gemma)0.105
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.105
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0040.002
Science and technology studies0.0030.003
Scholarly communication0.0090.005
Open science0.0050.003
Research integrity0.0200.031
Insufficient payload (model declined to judge)0.0090.006

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 head0.327
Teacher spread0.311 · 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
GenreEditorial

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

Citations85
Published2020
Admission routes1
Has abstractno

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