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Record W2979760837 · doi:10.1182/blood.v114.22.649.649

Reducing the Risk for Transplant Related Mortality After Allogeneic Hematopoietic Cell Transplantation: How Much Progress Has Been Made?.

2009· article· en· W2979760837 on OpenAlexaff
John Horan, Brent R. Logan, Afiba Manza‐A. Agovi, Hillard M. Lazarus, Andrea Bacigalupo, Karen K. Ballen, Rodrigo Martino, Mark Juckett, H. Jean Khoury, Christopher Bredeson, Vikas Gupta, Franklin O. Smith, Gregory A. Hale, Matthew Carabasi, Philip L. McCarthy, J. Douglas Rizzo, Marcelo C. Pasquini

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineInternal medicineHazard ratioProportional hazards modelTransplantationIncidence (geometry)Graft-versus-host diseaseSurgeryOncologyConfidence interval

Abstract

fetched live from OpenAlex

Abstract Abstract 649 TRM is among the major challenges for the success of HCT. Over the past two decades advances in the prevention and treatment of the major sources of TRM; regimen related toxicity, graft-versus-host disease (GVHD) and infections, have been made. To estimate the combined effect of these advances over time, we assessed changes in the incidence of TRM from 1985 through 2004 in 5,972 patients younger than 50 years, who received bone marrow (BM) or peripheral blood (PB) HCT with myeloablative conditioning for AML in first (CR1) or second (CR2) complete remission reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). The incidence of TRM was determined for four consecutive five-year periods for HLA-matched sibling donors (MRD) and the later three for unrelated donor (URD) separately by donor type and disease status at transplant. Cox proportional hazard regression models of TRM and overall survival outcomes were constructed with time periods as the main effect. Adjustments for patient and disease characteristics including age, performance score, coexistent diseases and cytogenetics were made in all multivariate models. Subgroup analyses were performed to account for the influence of major changes in transplant characteristics over time, i.e. GVHD prophylaxis, graft source and HLA matching. We observed a steady drop in the risk of TRM over time among patients in CR1 and CR2 receiving MRD transplants, which was associated with a significant reduction in risk of death (table below). Among URD recipients, TRM also improved with lower RR in 2000-2004 compared to earlier periods. No improvements in long term OS was observed in URD CR1 group. For patients in CR2, the RR for overall mortality was 0.74 (0.6-0.9, p=0.03) for 2000-2004 compared to 1990-1994. Subgroup analyses restricted to recipients of BM grafts, cyclosporine/methotrexate for all transplants and partially HLA-matched grafts for URD resulted in similar trends, suggesting that improvements in TRM were not solely related to utilization of PB, newer GVHD prophylaxis or better HLA matching. In conclusion, our results demonstrate lower risk for TRM over time in patients receiving HCT from MRD and URD for AML in CR1 and CR2. These reductions in risk of TRM have been accompanied by reduced risk of overall mortality in most groups of patients studied. Disclosures: No relevant conflicts of interest to declare.

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)0.005
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.257
Teacher spread0.237 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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