MétaCan
Menu
← Back to cohort

Transplantation-Specific Cytogenetics Grouping Scheme for Patients with Myelodysplastic Syndromes: A Multicenter Validation Study

2008· article· en· W2512584198 on OpenAlexaff
Philippe Armand, H. Joachim Deeg, Haesook T. Kim, Hun Lee, Paul M. Armistead, Marcos de Lima, Vikas Gupta, Robert J. Soiffer

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsCytogeneticsMyelodysplastic syndromesTransplantationMedicineInternal medicineOncologySingle CenterInternational Prognostic Scoring SystemBone marrowBiology

Abstract

fetched live from OpenAlex

Abstract Cytogenetics are an important prognostic factor for patients with myelodysplastic syndromes (MDS). However, the most commonly employed cytogenetics grouping scheme, as used in the IPSS, was derived from a cohort of patients who primarily received supportive care. This scheme may therefore not be optimal for stratifying patients undergoing aggressive therapy such as allogeneic stem cell transplantation (SCT). We previously proposed an SCT-specific cytogenetics grouping scheme for patients with MDS and AML arising from MDS (mAML), based on single-institution data. That scheme allowed for better prognostic stratification than the IPSS scheme. We undertook the present retrospective multicenter study to validate those results. Included were 546 patients with MDS or mAML from the Fred Hutchinson Cancer Center, the M.D. Anderson Cancer Center, and Princess Margaret Hospital. The median age was 53 years (range 18–74). 27% of patients had high-risk MDS (RAEB 1 or 2), and 43% had mAML; 17% had therapy-related disease. Overall 61% of patients were untreated at the time of SCT, while 12% were in CR following treatment. 68% received a conventional intensity conditioning regimen, and 65% were transplanted with peripheral blood stem cells. Donors were matched related (46%), matched unrelated (36%), or mismatched (18%). Cytogenetics were available for 86% of patients. Of those, 47% had favorable, 25% intermediate, and 28% adverse cytogenetics, when grouped by IPSS category. With a median follow-up of 48 months, 4-year relapse-free and overall survivals were 36% and 40%, respectively. In multivariate analyses, variables significantly associated with overall survival were cytogenetics, disease type and stage, patient age, donor match, and year of transplantation. Notably, therapy-related disease was not associated with increased mortality in this model. The optimal cytogenetics grouping scheme comprised two groups, with abnormalities of chromosome 7 and complex karyotype being adverse, and all other abnormalities (including normal karyotype, del(5q), and del(20q)) being standard risk. Adverse cytogenetics was the strongest prognostic factor for SCT outcome in this cohort, with a hazard ratio for mortality of 2.1 (p<0.0001). Four-year relapse-free and overall survivals were 42% and 46%, respectively, in the standard risk group, versus 21% and 23% in the adverse group (p<0.0001 for both comparisons). These differences were solely due to an increased risk of relapse in the adverse group (with a 4-year cumulative relapse incidence of 41%, versus 24% in the standard risk group (p<0.0001)), while non-relapse mortality was the same for both groups (38% and 38%, p=0.6). This grouping scheme retained its prognostic significance irrespective of patient age, disease type (low-risk versus high-risk MDS versus mAML), therapy-related or de novo disease, and conditioning intensity. Based on this multicenter validation, we propose that this SCT-specific cytogenetics grouping scheme be used for patients with MDS or mAML who are considering or undergoing SCT, for prognostication, patient selection, outcome reporting, or clinical trial stratification purposes.

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)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.033
GPT teacher head0.275
Teacher spread0.242 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicAcute Myeloid Leukemia Research→French-language works237,207→