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Record W2770831277 · doi:10.1016/j.bbmt.2017.11.004

Influence of Age on Acute and Chronic GVHD in Children Undergoing HLA-Identical Sibling Bone Marrow Transplantation for Acute Leukemia: Implications for Prophylaxis

2017· article· en· W2770831277 on OpenAlexaff
Muna Qayed, Tao Wang, Michael Hemmer, Stephen R. Spellman, Mukta Arora, Daniel R. Couriel, Amin M. Alousi, Joseph Pidala, Hisham Abdel‐Azim, Mahmoud Aljurf, Mouhab Ayas, Menachem Bitan, Mitchell S. Cairo, Sung Won Choi, Christopher E. Dandoy, David Delgado, Robert Peter Gale, Gregory A. Hale, Haydar Frangoul, Rammurti T. Kamble, Mohamed A. Kharfan‐Dabaja, Leslie Lehman, John E. Levine, Margaret L. MacMillan, David I. Marks, Taiga Nishihori, Richard F. Olsson, Peiman Hematti, Olle Ringdén, Ayman Saad, Prakash Satwani, Bipin N. Savani, Kirk R. Schultz, Sachiko Seo, Shalini Shenoy, Edmund K. Waller, Lolie C. Yu, Mary M. Horowitz, John Horan

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2017
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 DiseasesOffice of Naval ResearchNational Cancer InstituteHealth ResearchNational Institutes of HealthNational Heart, Lung, and Blood InstituteGovernment of South AustraliaGilead SciencesActinium PharmaceuticalsU.S. NavyU.S. Department of DefenseAmgen
KeywordsMedicineCumulative incidenceHazard ratioCalcineurinInternal medicineTransplantationIncidence (geometry)SiblingGraft-versus-host diseaseHematopoietic stem cell transplantationLeukemiaGastroenterologyAcute leukemiaBone marrowImmunologyConfidence interval

Abstract

fetched live from OpenAlex

Relapse remains the major cause of mortality after hematopoietic cell transplantation (HCT) for pediatric acute leukemia. Previous research has suggested that reducing the intensity of calcineurin inhibitor-based graft-versus-host disease (GVHD) prophylaxis may be an effective strategy for abrogating the risk of relapse in pediatric patients undergoing matched sibling donor (MSD) HCT. We reasoned that the benefits of this strategy could be maximized by selectively applying it to those patients least likely to develop GVHD. We conducted a study of risk factors for GVHD, to risk-stratify patients based on age. Patients age <18 years with leukemia who received myeloablative, T cell-replete MSD bone marrow transplantation and calcineurin inhibitor-based GVHD prophylaxis between 2000 and 2013 and were entered into the Center for International Blood and Marrow Transplant Research registry were included. The cumulative incidence of grade II-IV acute GVHD (aGVHD) was 19%, that of grade II-IV aGVHD 7%, and that of chronic GVHD (cGVHD) was 16%. Compared with age 13 to 18 years, age 2 to 12 years was associated with a lower risk of grade II-IV aGVHD (hazard ratio [HR], .42; 95% confidence interval [CI], .26 to .70; P = .0008), grade II-IV aGVHD (HR, .24; 95% CI, .10 to .56; P = .001), and cGVHD (HR, .32; 95% CI, .19 to .54; P < .001). Compared with 2000-2004, the risk of grade II-IV aGVHD was lower in children undergoing transplantation in 2005-2008 (HR, .36; 95% CI, .20 to .65; P = .0007) and in 2009-2013 (HR, .24; 95% CI. .11 to .53; P = .0004). Similarly, the risk of grade III-IV aGVHD was lower in children undergoing transplantation in 2005-2008 (HR, .23; 95% CI, .08 to .65; P = .0056) and 2009-2013 (HR, .16; 95% CI, .04 to .67; P = .0126) compared with those doing so in 2000-2004. We conclude that aGVHD rates have decreased significantly over time, and that children age 2 to 12 years are at very low risk for aGVHD and cGVHD. These results should be validated in an independent analysis, because these patients with high-risk malignancies may be good candidates for trials of reduced GVHD prophylaxis.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.298
Teacher spread0.284 · 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 designObservational
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

Citations57
Published2017
Admission routes1
Has abstractyes

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