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Record W3087887906 · doi:10.3390/gidisord2040030

The Effects of Iron Supplementation and Fortification on the Gut Microbiota: A Review

2020· review· en· W3087887906 on OpenAlexaff
Emma Finlayson‐Trick, Jordie A. J. Fischer, David A. Goldfarb, Crystal D Karakochuk

Bibliographic record

VenueGastrointestinal Disorders · 2020
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsGut floraIron supplementationPrebioticIron homeostasisIron deficiencyGut bacteriaBiologyDietary ironHepcidinFood fortificationImmunologyPhysiologyMedicineAnemiaFood scienceEnvironmental healthInflammationInternal medicineEndocrinologyPopulationMetabolism

Abstract

fetched live from OpenAlex

Iron supplementation and fortification are used to treat iron deficiency, which is often associated with gastrointestinal conditions, such as inflammatory bowel disease and colorectal cancer. Within the gut, commensal bacteria contribute to maintaining systemic iron homeostasis. Disturbances that lead to excess iron promote the replication and virulence of enteric pathogens. Consequently, research has been interested in better understanding the effects of iron supplementation and fortification on gut bacterial composition and overall gut health. While animal and human trials have shown seemingly conflicting results, these studies emphasize how numerous factors influence gut microbial composition. Understanding how different iron formulations and doses impact specific bacteria will improve the outcomes of iron supplementation and fortification in humans. Furthermore, discerning the nuances of iron supplementation and fortification will benefit subpopulations that currently do not respond well to treatment.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.915
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.303
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations28
Published2020
Admission routes1
Has abstractyes

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