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Chronic GvHD NIH Consensus Project Biology Task Force: evolving path to personalized treatment of chronic GvHD

2022· article· en· W4308056077 on OpenAlexafffund
Nataliya P. Buxbaum, Gèrard Socié, Geoffrey R. Hill, Kelli P. A. MacDonald, Victor Tkachev, Takanori Teshima, Stephanie J. Lee, Jerome Ritz, Stefanie Sarantopoulos, Leo Luznik, Defu Zeng, Sophie Paczesny, Paul J. Martin, Steven Z. Pavletic, Kirk R. Schultz, Bruce R. Blazar

Bibliographic record

VenueBlood Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Cancer InstituteCanadian Institutes of Health ResearchPharmacyclicsGenentechSyndax PharmaceuticalsNational Institutes of HealthIncyteTherakosFlorida Department of HealthGilead SciencesAmgenNational Heart, Lung, and Blood InstitutePfizerNational Institute of Allergy and Infectious DiseasesCelgeneAstraZenecaEli Lilly and Company
KeywordsDiseaseMedicineIntensive care medicineHematopoietic stem cell transplantationConsensus conferenceGraft-versus-host diseasePerspective (graphical)Stem cellImmunologyBioinformaticsPathologyBiologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

Chronic graft-versus-host disease (cGvHD) remains a prominent barrier to allogeneic hematopoietic stem cell transplantion as the leading cause of nonrelapse mortality and significant morbidity. Tremendous progress has been achieved in both the understanding of pathophysiology and the development of new therapies for cGvHD. Although our field has historically approached treatment from an empiric position, research performed at the bedside and bench has elucidated some of the complex pathophysiology of cGvHD. From the clinical perspective, there is significant variability of disease manifestations between individual patients, pointing to diverse biological underpinnings. Capitalizing on progress made to date, the field is now focused on establishing personalized approaches to treatment. The intent of this article is to concisely review recent knowledge gained and formulate a path toward patient-specific cGvHD therapy.

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)0.036
metaresearch head score (Gemma)0.030
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: Review · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0070.004
Open science0.0040.006
Research integrity0.0100.019
Insufficient payload (model declined to judge)0.0090.005

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 head0.020
GPT teacher head0.308
Teacher spread0.289 · 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
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

Citations32
Published2022
Admission routes2
Has abstractyes

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