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Record W2120504920 · doi:10.1016/j.bbmt.2014.11.676

Rabbit Anti–T Cell Globulin in Allogeneic Hematopoietic Cell Transplantation

2014· review· en· W2120504920 on OpenAlexafffund
Jan Storek, Mohamad Mohty, Jaap Jan Boelens

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2014
Typereview
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates - Health SolutionsAlberta Cancer Foundation
KeywordsMedicineHematopoietic cellGlobulinTransplantationImmunologyHaematopoiesisHematopoietic stem cell transplantationRabbit (cipher)Stem cellInternal medicineCell biologyBiology

Abstract

fetched live from OpenAlex

Anti-T cell globulin (ATG) is polyclonal IgG from rabbits immunized with human thymocytes or a human T cell line. Prophylaxis using ATG infused with conditioning for adult marrow or blood stem cell transplantation reduces both acute and chronic graft-versus-host disease (GVHD). However, ATG is not or minimally efficacious in steroid refractory GVHD treatment. Regarding preemptive therapy, ATG is promising; however, further work is needed on establishing adequate biomarkers to be used as triggers for preemptive therapy before it can be used routinely. Relapse is not increased by ATG, except possibly in the setting of reduced-intensity conditioning. Infections are probably increased when using high but not low-dose ATG, except for Epstein-Barr virus-driven post-transplantation lymphoproliferative disorder, which may be increased even with low-dose ATG. Survival is not improved with ATG; however, survival free of immunosuppressive therapy is improved. Pharmacokinetics of ATG are highly variable, resulting in highly variable areas under the time-concentration curves. Optimized dosing of ATG might improve transplantation outcomes. In conclusion, ATG reduces GVHD and, thus, may improve quality of life, without compromising survival.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.429
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.001
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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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.

Study designBench or experimental
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

Citations107
Published2014
Admission routes2
Has abstractyes

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