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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
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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.049
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 teacher head, not a consensus.

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

Citations11
Published2022
Admission routes1
Has abstractno

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