MétaCan
Menu
Back to cohort
Record W2049622469 · doi:10.1016/j.bbmt.2014.01.002

Immune Cell Subset Counts Associated with Graft-versus-Host Disease

2014· article· en· W2049622469 on OpenAlexafffund
Peter Podgorny, Yiping Liu, Poonam Dharmani‐Khan, Laura M. Pratt, Kareem Jamani, Joanne Luider, Iwona Auer-Grzesiak, Adnan Mansoor, Tyler Williamson, Alejandra Ugarte-Torres, Mette Hoegh-Petersen, Douglas A. Stewart, Andrew Daly, Faisal Khan, James A. Russell, Jan Storek

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2014
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity of Calgary
FundersFondation pour la Recherche MédicaleAlberta Cancer Foundation
KeywordsImmunologyPathogenesisCD8MedicineImmune systemGraft-versus-host diseaseFlow cytometryTransplantationDendritic cellCytotoxic T cellBiologyInternal medicine

Abstract

fetched live from OpenAlex

Graft-versus-host disease (GVHD) is a major transplantation complication. The purpose of this study was to measure immune cell subsets by flow cytometry early after transplantation (before median day of GVHD onset) to identify subsets that may play a role in GVHD pathogenesis. We also measured the subsets later after transplantation to determine which subsets may be influenced by GVHD or its treatment. We studied 219 patients. We found that acute GVHD (aGVHD) was preceded by high counts of CD4 T cells and CD8 T cells. It was followed by low counts of total and naive B cells, total and cytolytic NK cells, and myeloid and plasmacytoid dendritic cells. Chronic GVHD (cGVHD) was preceded by low counts of memory B cells. In conclusion, both CD4 and CD8 T cells appear to play a role in the pathogenesis of aGVHD. Generation of B cells, NK cells, and dendritic cells may be hampered by aGVHD and/or its treatment. Memory B cells may inhibit the development of cGVHD.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.298

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 head0.010
GPT teacher head0.226
Teacher spread0.216 · 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 designBench or experimental
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

Citations58
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueBiology of Blood and Marrow TransplantationSame topicHematopoietic Stem Cell TransplantationFrench-language works237,207