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Record W4306941118 · doi:10.1038/s41375-022-01724-9

Finding consistency in classifications of myeloid neoplasms: a perspective on behalf of the International Workshop for Myelodysplastic Syndromes

2022· letter· en· W4306941118 on OpenAlexaff
Amer M. Zeidan, Jan Philipp Bewersdorf, Rena Buckstein, Mikkael A. Sekeres, David P. Steensma, Uwe Platzbecker, Sanam Loghavi, Jacqueline Boultwood, Rafael Bejar, John M. Bennett, Uma Borate, Andrew M. Brunner, Hetty E. Carraway, Jane E. Churpek, Naval Daver, Matteo Giovanni Della Porta, Amy E. DeZern, Fabio Efficace, Pierre Fenaux, María E. Figueroa, Peter L. Greenberg, Elizabeth A. Griffiths, Stephanie Halene, Robert P. Hasserjian, Christopher S. Hourigan, Nina Kim, Tae Kon Kim, Rami S. Komrokji, Vijay Kutchroo, Alan F. List, Richard F. Little, Ravindra Majeti, Aziz Nazha, Stephen D. Nimer, Olatoyosi Odenike, Eric Padron, Mrinal M. Patnaik, Gail J. Roboz, David A. Sallman, Guillermo Sanz, Maximilian Stahl, Daniel T. Starczynowski, Justin Taylor, Zhuoer Xie, Mina L. Xu, Michael R. Savona, Andrew H. Wei, Omar Abdel‐Wahab, Valeria Santini

Bibliographic record

VenueLeukemia · 2022
Typeletter
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersTakeda OncologyGenentechNational Institutes of HealthAstellas PharmaPfizerIncyteCTI Biopharmabluebird bioAgios PharmaceuticalsSyndax PharmaceuticalsNational Heart, Lung, and Blood InstituteEdward P. Evans FoundationAcceleronAstex PharmaceuticalsDaiichi-SankyoMacroGenicsCelldex TherapeuticsJazz PharmaceuticalsLeukemia and Lymphoma SocietyApellis PharmaceuticalsAlexion PharmaceuticalsDaiichi Sankyo EuropeServierAplastic Anemia and MDS International FoundationGilead SciencesBioCrystCelgeneBristol-Myers SquibbAstraZenecaCardinal HealthAmgenAmerican Cancer Society
KeywordsMyelodysplastic syndromesConsistency (knowledge bases)MedicineMyeloidPerspective (graphical)OncologyMyeloid leukemiaInternal medicineImmunologyComputer scienceBone marrowArtificial intelligence

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)0.103
metaresearch head score (Gemma)0.321
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.103
Threshold uncertainty score0.543

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1030.321
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.004
Science and technology studies0.0100.016
Scholarly communication0.0150.017
Open science0.0100.009
Research integrity0.0550.072
Insufficient payload (model declined to judge)0.0040.002

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 head0.052
GPT teacher head0.328
Teacher spread0.276 · 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
GenreCommentary

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

Citations28
Published2022
Admission routes1
Has abstractno

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