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Coalescence of the German-Austrian and IMRAW Cytogenetic MDS Databases: Modification of Patient Risk Groups.

2007· article· en· W2589020689 on OpenAlexaff
Christian Steidl, Julie Schanz, Michelle M. Le Beau, John M. Bennett, Ulrich Germing, Peter L. Greenberg, Detlef Haase

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsInternational Prognostic Scoring SystemInternal medicineCohortMyelodysplastic syndromesKaryotypeOncologyMedicineBone marrowPediatricsBiologyChromosomeGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction The International Prognostic Scoring System (IPSS) for evaluating prognosis in myelodysplastic syndromes (MDS) has been the standard for risk assessment in this disease for the past ten years. Based on a patient cohort comprising 816 primary MDS patients from the IMRAW, a refined bone marrow cytogenetic classification system was introduced. Recently, the GACMSG published cytogenetic data including 1155 primary MDS patients treated with supportive care only. Coalescence of these two large databases offered the opportunity to analyze the cytogenetic data jointly and to propose a modified cytogenetic risk stratification system. Patients and Methods 1971 patients with karyotype and survival data originating from the IMRAW and the GACMSG cohorts were included in this study. The collectives comprised patients with primary MDS treated with supportive care, only allowing short courses of low dose oral chemotherapy or hemopoietic growth factors. By reviewing the ISCN karyotypes, the patients were grouped into cytogenetic categories defined by median survival (MS) (Haase et al, Blood in press). The categories comprised karyotypes with the respective abnormality alone or in combination with one additional anomaly. Karyotypes with 3, or more than 3 abnormalities were considered separate categories. Results We found 15 cytogenetic categories each comprising 10 or more patients. These categories could be combined into 4 prognostic groups according to the MS: Group 1 (MS>3 years): normal karyotype, del(5q), del(12p), del(20q), +21, −Y, −X; Group 2 (1.5–3 years): +1/+1q/t(1q), add(3q)/inv(3q)/del(3q)/t(3q), +8, del(11q); Group 3 (1–1.5 years): 3 anomalies, −7, del(7q); Group 4 (MS<1 year): >3 anomalies. Further stratification of these categories led to a system with 4 distinct risk strata (number of patients): good (1374), int-1 (160), int-2 (99), and poor (166). Only 172 patients (9% of all patients) could not be classified according to this system. Survival analysis of these 4 groups showed distinct MS (Log-rank test: p<0.0001): good, 50 months; int-1, 24 months; int-2, 15 months; poor, 6 months. When combining the non-classified patients into one group MS was 31 months. When comparing this new classification system with the original system defined by the IPSS, 66 formerly intermediate risk patients shifted into the good risk group and 114 poor risk patients into the intermediate risk group. Discussion Combined examination of the two databases introduces 7 new cytogenetic categories with distinct survival times as compared to the IPSS; Group 1: del(12p), +21, −X; Group 2: +1/+1q/t(1q), add(3q)/inv(3q)/del(3q)/t(3q), del(11q); Group 3: 3 anomalies. Based on previously published data, the proposed system combines non-complex karyotypes in one category and distinguishes karyotypes with 3 or more than 3 abnormalities. With respect to future refined integrative scoring in MDS we present an approach that distinguishes groups of intermediate risk and a heterogeneous group of as yet unclassified rare cases harboring uncertain prognoses. In the latter cases, risk assessment should be based on other prognostic parameters rather than assigning an intermediate risk to this group. This new cytogenetic risk stratification system needs to be validated and tested using multivariate approaches.

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)0.017
metaresearch head score (Gemma)0.049
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 score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.014
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.003
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 head0.028
GPT teacher head0.313
Teacher spread0.286 · 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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Citations5
Published2007
Admission routes1
Has abstractyes

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