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Record W2012434943 · doi:10.1016/j.bbmt.2014.12.031

NIH Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: II. The 2014 Pathology Working Group Report

2015· article· en· W2012434943 on OpenAlexaff
Howard M. Shulman, Diana M. Cardona, Joel K. Greenson, Sangeeta Hingorani, Thomas Horn, Elisabeth Huber, Andreas Kreft, Thomas Longerich, Thomas Hellman Morton, David Myerson, Víctor G. Prieto, Avi Z. Rosenberg, Nathaniel S. Treister, Kay Washington, Mirjana Ziemer, Steven Z. Pavletic, Stephanie J. Lee, Mary E.D. Flowers, Kirk R. Schultz, Madan Jagasia, Paul J. Martin, Georgia B. Vogelsang, David E. Kleiner

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineDiseaseClinical trialHost (biology)PathologyGraft-versus-host disease

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)0.385
metaresearch head score (Gemma)0.501
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.385
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3850.501
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0120.014
Bibliometrics0.0100.014
Science and technology studies0.0060.008
Scholarly communication0.0160.006
Open science0.0190.014
Research integrity0.0250.025
Insufficient payload (model declined to judge)0.0090.009

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 head0.210
GPT teacher head0.436
Teacher spread0.226 · 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.

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

Citations315
Published2015
Admission routes1
Has abstractno

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