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Record W2891400988 · doi:10.3324/haematol.2018.196295

Prevalence and management of iron overload in pyruvate kinase deficiency: report from the Pyruvate Kinase Deficiency Natural History Study

2018· letter· en· W2891400988 on OpenAlexafffund
Eduard J. van Beers, Stephanie van Straaten, D. Holmes Morton, Wilma Barcellini, Stefan Eber, Bertil Glader, Hassan M. Yaish, Satheesh Chonat, Janet L. Kwiatkowski, Jennifer Rothman, Mukta Sharma, Ellis J. Neufeld, Sujit Sheth, Jenny M. Despotovic, Nina Kollmar, Dagmar Pospı́šilová, Christine Knoll, Kevin H.M. Kuo, Yves Pastore, Alexis A. Thompson, Peter E. Newburger, Yaddanapudi Ravindranath, Winfred C. Wang, Marcin W. Włodarski, Heng Wang, Susanne Holzhauer, Vicky R. Breakey, Madeleine Verhovsek, Joachim B. Kunz, Melissa A. McNaull, Melissa J. Rose, Heather A. Bradeen, Kathryn Addonizio, Anran Li, Hasan Al‐Sayegh, Wendy B. London, Rachael F. Grace

Bibliographic record

VenueHaematologica · 2018
Typeletter
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityUniversity of TorontoUniversity Health Network
FundersMedizinische Fakultät der Albert-Ludwigs-Universität FreiburgSchool of Medicine, Emory UniversityUniversität HeidelbergChildren's Healthcare of AtlantaUniversity of VermontMcMaster UniversitySt. Jude Children's Research HospitalUniversity of MissouriOhio State UniversityWeill Cornell Medical CollegeUniversity of PennsylvaniaChildren's Hospital of MichiganChildren's Mercy HospitalWayne State UniversityPerelman School of Medicine, University of PennsylvaniaNationwide Children's HospitalChildren's Hospital of PhiladelphiaUniversity of TorontoAlbert-Ludwigs-Universität FreiburgEmory UniversityAflac
KeywordsPyruvate kinase deficiencyPyruvate kinasePKM2Dihydrolipoyl transacetylasePyruvate dehydrogenase complexNatural historyPyruvate decarboxylationMedicineChemistryGlycolysisBiochemistryInternal medicineEnzyme

Abstract

fetched live from OpenAlex

Pyruvate kinase (PK) deficiency is the most common red cell glycolytic enzyme defect causing hereditary non-spherocytic hemolytic anemia. Current treatments are mainly supportive and include red cell transfusions and splenectomy.11 Regular red cell transfusions are known to result in iron overload; however, the prevalence and spectrum of transfusion-independent iron overload in the overall PK deficient population has not been well defined. This analysis describes the prevalence and clinical characteristics of iron overload in patients enrolled in the PK Deficiency Natural History Study (NHS) with a focus on those patients who are not regularly transfused.2

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)0.000
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.264
Teacher spread0.233 · 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

Citations60
Published2018
Admission routes2
Has abstractyes

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