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Advancing drug development in myelodysplastic syndromes

2024· review· en· W4405906667 on OpenAlexaff
Alain Mina, Kathy L. McGraw, Lea Cunningham, Nina Kim, Emily Y. Jen, Katherine R. Calvo, Lori A. Ehrlich, Peter D. Aplan, Guillermo Garcia‐Manero, James M. Foran, Jacqueline S. Garcia, Amer M. Zeidan, Amy E. DeZern, Rami S. Komrokji, Mikkael A. Sekeres, Bart L. Scott, Rena Buckstein, Sara Tinsley-Vance, Amit Verma, Tanya Wroblewski, Steven Z. Pavletic, Kelly J. Norsworthy

Bibliographic record

VenueBlood Advances · 2024
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreSunnybrook Health Science Centre
FundersGenentechU.S. Food and Drug AdministrationNational Institutes of HealthParacelsus Medizinische PrivatuniversitätCincinnati Children's Hospital Medical CenterUniversità degli Studi di FirenzeNational Heart, Lung, and Blood InstituteAcceleronEli Lilly and CompanyUniversidad de SalamancaMemorial Sloan-Kettering Cancer CenterUniversity of WashingtonBristol-Myers SquibbCleveland ClinicUniversity of MiamiPfizerNorthwestern UniversityIncyteNational Cancer InstituteServierOhio State UniversityUniversity of PennsylvaniaAlexion PharmaceuticalsMassachusetts General HospitalSanofiAstraZenecaCelgene
KeywordsMyelodysplastic syndromesMedicineDecitabineDrug developmentClinical trialOncologyStem cellTransplantationIntensive care medicineHematopoietic stem cell transplantationFood and drug administrationDrugInternal medicinePharmacologyBone marrow

Abstract

fetched live from OpenAlex

ABSTRACT: Myelodysplastic syndromes/neoplasms (MDSs) are heterogeneous stem cell malignancies characterized by poor prognosis and no curative therapies outside of allogeneic hematopoietic stem cell transplantation. Despite some recent approvals by the US Food and Drug Administration, (eg, luspatercept, ivosidenib, decitabine/cedazuridine, and imetelstat), there has been little progress in the development of truly transformative therapies for the treatment of patients with MDS. Challenges to advancing drug development in MDS are multifold but may be grouped into specific categories, including criteria for risk stratification and eligibility, response definitions, time-to-event end points, transfusion end points, functional assessments, and biomarker development. Strategies to address these challenges and optimize future clinical trial design for patients with MDS are presented here.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.029
GPT teacher head0.361
Teacher spread0.332 · 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 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

Citations12
Published2024
Admission routes1
Has abstractyes

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