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Record W4386002608 · doi:10.1016/j.blre.2023.101128

Classification, risk stratification and response assessment in myelodysplastic syndromes/neoplasms (MDS): A state-of-the-art report on behalf of the International Consortium for MDS (icMDS)

2023· review· en· W4386002608 on OpenAlexafffund
Maximilian Stahl, Jan Philipp Bewersdorf, Zhuoer Xie, Matteo Giovanni Della Porta, Rami S. Komrokji, Mina L. Xu, Omar Abdel‐Wahab, Justin Taylor, David P. Steensma, Daniel T. Starczynowski, Mikkael A. Sekeres, Guillermo Sanz, David A. Sallman, Gail J. Roboz, Uwe Platzbecker, Mrinal M. Patnaik, Eric Padron, Olatoyosi Odenike, Stephen D. Nimer, Aziz Nazha, Ravindra Majeti, Sanam Loghavi, Richard F. Little, Alan F. List, Tae Kon Kim, Christopher S. Hourigan, Robert P. Hasserjian, Stephanie Halene, Elizabeth A. Griffiths, Steven D. Gore, Peter L. Greenberg, María E. Figueroa, Pierre Fenaux, Fabio Efficace, Amy E. DeZern, Naval Daver, Jane E. Churpek, Hetty E. Carraway, Rena Buckstein, Andrew M. Brunner, Jacqueline Boultwood, Uma Borate, Rafael Bejar, John M. Bennett, Andrew H. Wei, Valeria Santini, Michael R. Savona, Amer M. Zeidan

Bibliographic record

VenueBlood Reviews · 2023
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersTakeda OncologyGenentechNational Institutes of HealthFoundation MedicineSierra OncologyAssociazione Italiana per la Ricerca sul CancroAstellas PharmaDaiichi-SankyoMacroGenicsAlexion PharmaceuticalsApellis PharmaceuticalsArgenxPfizerIncyteCTI Biopharmabluebird bioAgios PharmaceuticalsSyndax PharmaceuticalsNational Heart, Lung, and Blood InstituteEdward P. Evans FoundationAcceleronLoxo OncologyAstex PharmaceuticalsLeukemia and Lymphoma SocietyDaiichi Sankyo EuropeServierGilead SciencesJazz PharmaceuticalsCelgeneBristol-Myers SquibbAstraZenecaAmgenGlaxoSmithKline
KeywordsMyelodysplastic syndromesMedicineRisk stratificationRisk assessmentInternational Prognostic Scoring SystemIntensive care medicineInternal medicineComputer science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.110
GPT teacher head0.412
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2023
Admission routes2
Has abstractno

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