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Record W2569343261 · doi:10.1182/blood.v120.21.432.432

Lille Scoring System Rather Than DIPSS Is a Better Predictive of Overall Mortality After Allogeneic Hematopoietic Cell Transplantation (HCT) for Primary Myelofibrosis Using Reduced Intensity Conditioning: A Report From the Center for International Blood and Marrow Transplant Research (CIBMTR)

2012· article· en· W2569343261 on OpenAlexaff
Vikas Gupta, Kwang Woo Ahn, Xiaochun Zhu, Zhen‐Huan Hu, Parameswaran Hari, Richard T. Maziarz, Jörge E. Cortes, Matt Kalaycio, Wael Saber

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineCumulative incidenceInternal medicineBusulfanFludarabineMyelofibrosisTransplantationInternational Prognostic Scoring SystemMelphalanHematopoietic cellConfidence intervalHematopoietic stem cell transplantationIncidence (geometry)GastroenterologySurgeryChemotherapyHaematopoiesisBone marrowCyclophosphamideStem cellMyelodysplastic syndromes

Abstract

fetched live from OpenAlex

Abstract Abstract 432 The Dynamic International prognostic scoring system (DIPSS) is increasingly being used as a prognostic tool for determining the risk of mortality for primary myelofibrosis (PMF), and has largely replaced Lille scoring system. However, it is unclear whether this scale can predict mortality after reduced intensity conditioning (RIC) allogeneic HCT, a procedure that is increasingly being utilized, as demonstrated by data from the CIBMTR. Using the CIBMTR database, the impact of patient, disease and transplant related factors on outcomes of 222 patients, who underwent HCT for PMF using RIC was analyzed. Median follow-up of survivors was 50 months (range, 3–165). Median age at HCT was 55 years, and 56 (25%) were >60 years. Donors were matched related donor (MRD), well-matched unrelated (MUD), and partially/mismatched unrelated (MMUD) in 85 (38%), 94 (42%), and 43 (19%), respectively. Conditioning regimens were: Fludarabine (Flu) and Melphalan (Mel), 62 (28%); Flu and Busulphan (Bu), 81 (36%), Flu and total body irradiation (TBI), 49 (22%); and others 30 (14%). Disease-risk status at HCT according to Lille scoring system was: low 48 (22%), intermediate (Int) 105 (47%), and high 69 (31%); and according to DIPSS was: low 25 (11%), int-1 110 (50%), int-2 81 (36%), and high, 4 (2%). The cumulative incidences of acute graft versus host disease (GvHD) at 100 days and chronic GvHD at 5-years were 48% (95% confidence intervals [CI] 41–54) and 50% (95% CI 43–57), respectively. The cumulative incidence of relapse/progression and non-relapse mortality (NRM) at 5-years was 28% (95% CI 22–34) and 38% (95% CI 31–44), respectively. The corresponding disease-free and overall survival was 34% (95% CI 28–40), and 37% (95% CI 31–44), respectively. In multivariate analysis, high-risk disease defined by Lille scoring system was associated with two-fold higher mortality compared to low-risk disease (Table). Higher risk disease status as defined by DIPSS was not associated with a significant increase in mortality when compared to lower-risk disease (Table). MUD and MMUD use were associated with higher mortality risk compared to MRD with relative risk (RR) of 1.59 (95% CI 1.00–2.52) and 2.6 (95% CI 1.56–4.35), respectively. A comparison of conditioning regimens demonstrated a trend towards reduced mortality with FluMel when compared to FluBu (RR 0.56, 95% CI 0.33–0.92; overall p=0.11), or other regimens (RR 0.51, 95% CI 0.26–0.99; overall p=0.11). In conclusion, the current study highlights that the DIPSS was limited in predicting the mortality after RIC transplantation for PMF, while the Lille scoring system remained predictive of mortality in high risk patients. These findings underscore the need for transplant-specific scoring system. Compared to other conditioning regimens FluMel appears to be associated with a trend towards better survival, which needs to be confirmed in prospective randomized trials. Table. Multivariate Analysis (MVA) for overall mortality* Model 1. MVA of Lille scoring system Variable Relative Risk (RR) 95% CI Overall p-value Lille-risk score low-risk (n = 48) 1 0.02 Intermediate-risk (n = 105) 1.47 0.84-2.58 High risk (n = 69) 2.22 1.23-4.00 Conditioning regimen Flu TBI 1 0.11 Flu Mel 0.67 0.38-1.19 Flu Bu 1.20 0.73-1.97 Others 1.30 0.68-2.48 Donor type HLA-identical sibling/other related 1 0.001 Well-matched URD 1.60 1.01-2.53 Partially matched/mismatched URD 2.61 1.57-4.36 Contrast Flu Mel vs. Flu Bu 0.56 0.33-0.93 0.03 Flu Mel vs. Others 0.51 0.27-0.99 0.05 Flu Bu vs. Others 0.92 0.52-1.66 0.79 Intermediate vs. High 0.66 0.43-1.01 0.06 Well-matched URD vs. Partially matched/mismatched URD 0.61 0.38-0.98 0.04 Model 2. MVA of DIPSS DIPSS Low/Int-1 (n = 135) 1 0.10 Int-2/high (n = 85) 1.39 0.94-2.043 * Adjusted for age, sex, Karnofsky performance score, platelet count, spleen status, conditioning regimen, donor type, GVHD prophylaxis and year of transplant. 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)0.001
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.044
GPT teacher head0.306
Teacher spread0.263 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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