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An agenda to advance research in myelodysplastic syndromes: a TOP 10 priority list from the first international workshop in MDS

2022· article· en· W4306843199 on OpenAlexaff
Maximilian Stahl, Omar Abdel‐Wahab, Andrew H. Wei, Michael R. Savona, Mina L. Xu, Zhuoer Xie, Justin Taylor, Daniel T. Starczynowski, Guillermo Sanz, David A. Sallman, Valeria Santini, Gail J. Roboz, Mrinal M. Patnaik, Eric Padron, Olatoyosi Odenike, Aziz Nazha, Stephen D. Nimer, Ravindra Majeti, Richard F. Little, Steven D. Gore, Alan F. List, Vijay Kutchroo, Rami S. Komrokji, Tae Kon Kim, Nina Kim, Christopher S. Hourigan, Robert P. Hasserjian, Stephanie Halene, Elizabeth A. Griffiths, Peter L. Greenberg, María E. Figueroa, Pierre Fenaux, Fabio Efficace, Amy E. DeZern, Matteo Giovanni Della Porta, Naval Daver, Jane E. Churpek, Hetty E. Carraway, Andrew M. Brunner, Uma Borate, John M. Bennett, Rafael Bejar, Jacqueline Boultwood, Sanam Loghavi, Jan Philipp Bewersdorf, Uwe Platzbecker, David P. Steensma, Mikkael A. Sekeres, Rena Buckstein, Amer M. Zeidan

Bibliographic record

VenueBlood Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersDaiichi Sankyo EuropeNational Cancer InstituteNational Institutes of HealthAstellas PharmaIncyteAstex PharmaceuticalsGilead SciencesJazz PharmaceuticalsLeukemia and Lymphoma SocietyAcceleronCelgeneBristol-Myers SquibbAstraZenecaNational Institute of Diabetes and Digestive and Kidney DiseasesCardinal HealthAmgenNational Heart, Lung, and Blood InstitutePfizerNational Center for Advancing Translational SciencesAgios Pharmaceuticals
KeywordsMyelodysplastic syndromesMEDLINEMedicineComputer sciencePolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Myelodysplastic syndromes (MDS) are neoplasms with high molecular, biological, and clinical heterogeneity.1,2 Consequently, conducting basic, translational, and clinical research on MDS has historically been challenging and the field has lagged behind in terms of achieving significant therapeutic advances.

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)0.067
metaresearch head score (Gemma)0.045
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.352

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.045
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0050.003
Science and technology studies0.0050.004
Scholarly communication0.0200.016
Open science0.0040.019
Research integrity0.0240.035
Insufficient payload (model declined to judge)0.0320.012

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 head0.049
GPT teacher head0.390
Teacher spread0.341 · 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
GenreOther

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

Citations11
Published2022
Admission routes1
Has abstractno

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