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Record W3096729878 · doi:10.1038/s41591-021-01238-4

Malaria is a cause of iron deficiency in African children

2021· article· en· W3096729878 on OpenAlexaff
John M. Muriuki, Alexander J. Mentzer, Ruth E. Mitchell, Emily L. Webb, Anthony Etyang, Catherine Kyobutungi, Alireza Morovat, Wandia Kimita, Francis M. Ndungu, Alex Macharia, Caroline Ngetsa, Johnstone Makale, Swaib A. Lule, Solomon K. Musani, Laura M. Raffield, Clare Cutland, Sodiomon B. Sirima, Amidou Diarra, Alfred B. Tiono, Michal Fried, M. Gwamaka, Seth Adu‐Afarwuah, James P. Wirth, Rita Wegmüller, Shabir A. Madhi, Robert W. Snow, Adrian V. S. Hill, Kirk A. Rockett, Manjinder S. Sandhu, Dominic Kwiatkowski, Andrew M. Prentice, Kendra Byrd, Alex Ndjebayi, Christine P. Stewart, Reina Engle‐Stone, Tim Green, Crystal D Karakochuk, Parminder S. Suchdev, Philip Bejon, Patrick E. Duffy, George Davey Smith, Alison M. Elliott, Thomas N. Williams, Sarah H. Atkinson

Bibliographic record

VenueNature Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
FundersFogarty International CenterNational Institute of Allergy and Infectious DiseasesNational Heart, Lung, and Blood InstituteMedical Research CouncilIrish AidAlliance for Accelerating Excellence in Science in AfricaAfrican Academy of SciencesNational Institute on Minority Health and Health DisparitiesNew Partnership for Africa's DevelopmentUniversity of GhanaUniversity of OxfordBill and Melinda Gates FoundationCenters for Disease Control and PreventionJackson State UniversityMississippi State Department of HealthEmory UniversityThrasher Research FundUNICEFWellcomeNational Institutes of HealthUnited States Agency for International DevelopmentU.S. Department of Health and Human ServicesWellcome TrustGovernment of the United KingdomDivision of Intramural Research, National Institute of Allergy and Infectious DiseasesNational Institute for Health and Care ResearchFoundation for the National Institutes of Health
KeywordsMalariaSickle cell traitMendelian randomizationMedicineObservational studyEnvironmental healthOdds ratioPublic healthIncidence (geometry)PediatricsBiologyImmunologyDiseaseInternal medicineGenotypeGeneticsPathology

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)0.000
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.272
Teacher spread0.265 · 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

Citations63
Published2021
Admission routes1
Has abstractno

Explore more

Same venueNature MedicineSame topicIron Metabolism and DisordersFrench-language works237,207