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Record W4307776418 · doi:10.1038/s41375-022-01738-3

Impact of pre-transplant induction and consolidation cycles on AML allogeneic transplant outcomes: a CIBMTR analysis in 3113 AML patients

2022· article· en· W4307776418 on OpenAlexfundno aff
Michael Boyiadzis, Mei‐Jie Zhang, Karen Chen, Hisham Abdel‐Azim, Muhammad Bilal Abid, Mahmoud Aljurf, Ulrike Bacher, Talha Badar, Sherif M. Badawy, Minoo Battiwalla, Nelli Bejanyan, Vijaya Raj Bhatt, Valerie I. Brown, Paul Castillo, Jan Černý, Edward A. Copelan, Charles Craddock, Bhagirathbhai Dholaria, Miguel Ángel Díaz, Christen L. Ebens, Robert Peter Gale, Siddhartha Ganguly, Lohith Gowda, Michael R. Grunwald, Shahrukh K. Hashmi, Gerhard Hildebrandt, Madiha Iqbal, Omer Jamy, Mohamed A. Kharfan‐Dabaja, Nandita Khera, Hillard M. Lazarus, Richard J. Lin, Dipenkumar Modi, Sunita Nathan, Taiga Nishihori, Sagar S. Patel, Attaphol Pawarode, Wael Saber, Akshay Sharma, Melhem Solh, John L. Wagner, Trent Wang, Kirsten M. Williams, Lena E. Winestone, Baldeep Wirk, Amer M. Zeidan, Christopher S. Hourigan, Mark R. Litzow, Partow Kebriaei, Marcos de Lima, Kristin Page, Daniel J. Weisdorf

Bibliographic record

VenueLeukemia · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
FundersCancer MoonshotSt. Jude Children's Research HospitalNational Institute of Allergy and Infectious DiseasesOffice of Naval ResearchLegend BiotechAgios PharmaceuticalsTakeda OncologyAstraZenecaGenentechAdaptive BiotechnologiesHealth Resources and Services AdministrationNational Institutes of HealthMorphoSysSeagenCTI BiopharmaCareDxBeiGeneSwedish Orphan BiovitrumAstellas PharmaGlaxoSmithKlineKiadis PharmaPharmacyclicsbluebird bioSanofiTG TherapeuticsMedacJazz PharmaceuticalsOmeros CorporationVertex PharmaceuticalsStemCyteCSL BehringNovartis Pharmaceuticals CorporationNational Center for Advancing Translational SciencesHistoGeneticsAtara BiotherapeuticsActinium PharmaceuticalsNational Cancer InstituteIncyteServierGilead SciencesMoonshot Research and Development ProgramCelgeneBristol-Myers SquibbAmgenNational Heart, Lung, and Blood InstitutePfizerMallinckrodt PharmaceuticalsAstellas Pharma US
KeywordsMedicineInternal medicineHematopoietic cellTransplantationOncologySurgeryHaematopoiesisStem cell

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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 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

Citations20
Published2022
Admission routes1
Has abstractno

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