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Record W2789218687 · doi:10.1016/j.bbmt.2018.02.007

Improving Revised International Prognostic Scoring System Pre-Allogeneic Stem Cell Transplantation Does Not Translate Into Better Post-Transplantation Outcomes for Patients with Myelodysplastic Syndromes: A Single-Center Experience

2018· article· en· W2789218687 on OpenAlexaff
Musa Alzahrani, Maryse Power, Yasser Abou Mourad, Michael J. Barnett, Raewyn Broady, Donna L. Forrest, Alina S. Gerrie, Donna E. Hogge, Stephen H. Nantel, David Sanford, Kevin Song, Heather J. Sutherland, Cynthia L. Toze, Thomas J. Nevill, Sujaatha Narayanan

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsBC Cancer AgencyUniversity of British Columbia
Fundersnot available
KeywordsMedicineInternational Prognostic Scoring SystemMyelodysplastic syndromesProportional hazards modelInternal medicineTransplantationHematopoietic stem cell transplantationHazard ratioOncologySingle CenterBone marrowConfidence interval

Abstract

fetched live from OpenAlex

The natural history of patients with myelodysplastic syndromes (MDS) is variable. The Revised International Prognostic Score (IPSS-R) is commonly used in practice to predict outcomes in patients with MDS at both diagnosis and before hematopoietic stem cell transplantation (HSCT). However, the effect of change in the IPSS-R before allogeneic HSCT with chemotherapy or hypomethylating agents on post-transplantation outcomes is currently unknown. We assessed whether improvement in IPSS-R prognostic score pre-HSCT would result in improvement in clinical outcomes post-HSCT. Secondary goals included studying the effect of prognostic factors on post-transplantation survival. All patients with MDS who underwent allogeneic HSCT at the Leukemia/BMT Program of British Columbia between February 1997 and April 2013 were included. Pertinent information was reviewed from the program database. IPSS-R was calculated based on data from the time of MDS diagnosis and before HSCT. Outcomes of patients who had improved IPSS-R pre-HSCT were compared with those with stable or worse IPSS-R. Overall survival (OS) and event-free survival (EFS) were estimated using the Kaplan-Meier method, with P values determined using the log-rank test. Hazard ratios were calculated using multivariable Cox proportional hazards regression models to study the effects of the prognostic variables on OS and EFS. A total of 138 consecutive patients were included. IPSS-R improved in 62 of these patients (45%), worsened in 23 (17%), remained stable in 41 (30%), and was unknown in 12 (9%). OS was not statistically different across the improved, worsened, and stable groups (30% versus 22% versus 40%, respectively; P = .63). The cumulative incidences of relapse and nonrelapse mortality at 5 years were 28.4% (95% confidence interval [CI], 21.1 to 36.1) and 31.6% (95% CI, 23.8 to 39.7), respectively. The rate of relapse was 23% in patients with <5% blasts at the time of HSCT, 69% in those with 5% to 20% blasts, and 66% in those with >20% blasts (P = .0004). In the entire cohort OS was 34% and EFS was 33%. There was no significant difference in outcomes between patients who received myeloablative conditioning and those who received nonmyeloablative conditioning before HSCT (OS, 34% and 39%, respectively; P = .63 and EFS, 34% and 32%, respectively; P = .86). OS was not statistically different among patients with improved, worsened, or stable IPSS-R. On multivariate analysis, only 3 factors were associated with OS: cytogenetic risk group at diagnosis, blast count at transplantation, and the presence or absence of chronic graft-versus-host disease. Improving IPSS-R before HSCT does not translate into better survival outcomes. Blast count pretransplantation was highly predictive of post-transplantation outcomes.

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 score0.136
Threshold uncertainty score0.963

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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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