MétaCan
Menu
Back to cohort
Record W4234665825 · doi:10.1530/rep-20-0271

Maternal iron homeostasis: effect on placental development and function

2020· review· en· W4234665825 on OpenAlexafffund
Hannah Roberts, Stephane L. Bourque, Stephen J. Renaud

Bibliographic record

VenueReproduction · 2020
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteUniversity of Alberta HospitalUniversity of AlbertaWestern University
FundersCanadian Institutes of Health Research
KeywordsPlacentaIron deficiencyPregnancyFetusOffspringMedicinePhysiologyIntrauterine growth restrictionObstetricsBiologyInternal medicineAnemia

Abstract

fetched live from OpenAlex

Iron is an essential mineral that participates in oxygen transport, DNA synthesis and repair, and as a cofactor for various cellular processes. Iron deficiency is the most common nutritional deficiency worldwide. Due to blood volume expansion and demands from the fetal-placental unit, pregnant women are one of the populations most at risk of developing iron deficiency. Iron deficiency during pregnancy poses major health concerns for offspring, including intrauterine growth restriction and long-term health complications. Although the underlying mechanisms remain unclear, maternal iron deficiency may indirectly impair fetal growth through changes in the structure and function of the placenta. Since the placenta forms the interface between mother and baby, understanding how the placenta changes in iron deficiency may yield new diagnostic indices of fetal stress in affected pregnancies, thereby leading to earlier interventions and improved fetal outcomes. In this review, we compile current data on the changes in placental development and function that occur under conditions of maternal iron deficiency, and discuss challenges and perspectives on managing the high incidence of iron deficiency in pregnant women.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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.

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

Citations32
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueReproductionSame topicIron Metabolism and DisordersFrench-language works237,207