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Record W4399856152 · doi:10.1007/s40471-024-00348-0

Vitamin D and Toxic Metals in Pregnancy - a Biological Perspective

2024· review· en· W4399856152 on OpenAlexafffund
Mandy Fisher, Hope A. Weiler, Jordan R. Kuiper, Michael M. Borghese, Jessie P. Buckley, Robin Shutt, Jillian Ashley‐Martin, Anita Subramanian, Tye E. Arbuckle, Beth K. Potter, Julian Little, Anne‐Sophie Morisset, Anne Marie Z. Jukic

Bibliographic record

VenueCurrent Epidemiology Reports · 2024
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsOttawa Public HealthUniversité LavalCanadian Public Health AssociationUniversity of OttawaHealth Canada
FundersNational Institute of Environmental Health SciencesHealth CanadaNational Institutes of Health
KeywordsPregnancyPerspective (graphical)MedicineEpidemiologyChemistryEnvironmental chemistryPhysiologyBiologyInternal medicineComputer scienceGenetics

Abstract

fetched live from OpenAlex

Purpose of Review: To discuss the potential biological mechanisms between vitamin D and toxic metals and summarize epidemiological studies examining this association in pregnant women. Recent Findings: We identified four plausible mechanisms whereby vitamin D and toxic metals may interact: nephrotoxicity, intestinal absorption of metals, endocrine disruption, and oxidative stress. Few studies have examined the association between vitamin D and toxic metals in pregnant women. North American studies suggest that higher vitamin D status early in pregnancy are associated with lower blood metals later in pregnancy. However, a trial of vitamin D supplementation in a pregnant population, with higher metal exposures and lower overall nutritional status, does not corroborate these findings. Summary: Given ubiquitous exposure to many toxic metals, nutritional intervention could be a means for prevention of adverse outcomes. Future prospective studies are needed to establish a causal relationship and clarify the directionality of vitamin D and metals. Supplementary Information: The online version contains supplementary material available at 10.1007/s40471-024-00348-0.

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)0.004
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.288
GPT teacher head0.512
Teacher spread0.225 · 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 designOther design
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

Citations5
Published2024
Admission routes2
Has abstractyes

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