MétaCan
Menu
Back to cohort

Nutritional Aspects of Iron in Health and Disease

2023· preprint· en· W4366815141 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsMcGill UniversityJewish General Hospital
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsHepcidinHemochromatosisHereditary hemochromatosisMedicinePhysiologyDiseaseAnemiaDietary ironEndocrine systemIron deficiencyPopulationHormoneEndocrinologyBioinformaticsInternal medicineBiologyEnvironmental health

Abstract

fetched live from OpenAlex

Dietary iron assimilation is critical for health and essential to prevent iron deficient states and related comorbidities, such as anemia. The bioavailability of iron is limited, while its absorption and metabolism are tightly controlled to keep body iron stores within a relatively narrow range. Genetic inactivation of the iron hormone hepcidin causes hereditary hemochromatosis, an endocrine disorder of iron overload characterized by chronic hyperabsorption of dietary iron, with deleterious clinical complications if untreated. The impact of high dietary iron intake and elevated body iron stores in the general population is not well understood. Herein, we summarize epidemiological data suggesting that high intake of heme iron, which is abundant in meat products, poses a risk factor for metabolic syndrome pathologies, cardiovascular diseases, and some cancers. We discuss clinical relevance and potential limitations of data from cohort studies, as well as the need to establish causality and elucidate molecular mechanisms.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.104
GPT teacher head0.369
Teacher spread0.264 · 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