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Record W4295007776 · doi:10.1515/tnsci-2022-0242

Perinatal diet and offspring anxiety: A scoping review

2022· review· en· W4295007776 on OpenAlexaff
Sasha Monteiro, Yousef Sadat Nejad, Monique Aucoin

Bibliographic record

VenueTranslational Neuroscience · 2022
Typereview
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsCanadian College of Naturopathic Medicine
Fundersnot available
KeywordsOffspringAnxietyPregnancyLactationPsychological interventionAnimal studiesMedicinePsychologyPhysiologyEndocrinologyBiologyPsychiatry

Abstract

fetched live from OpenAlex

Health behaviors during pregnancy have an impact on the developing offspring. Dietary factors play a role in the development of mental illness: however, less is known about the impact of diet factors during pre-conception, gestation, and lactation on anxiety levels in offspring. This scoping review sought to systematically map the available research involving human and animal subjects to identify nutritional interventions which may have a harmful or protective effect, as well as identify gaps. Studies investigating an association between any perinatal diet pattern or diet constituent and offspring anxiety were included. The number of studies reporting an association with increased or decreased levels of anxiety were counted and presented in figures. A total of 55,914 results were identified as part of a larger scoping review, and 120 articles met the criteria for inclusion. A greater intake of phytochemicals and vitamins were associated with decreased offspring anxiety whereas maternal caloric restriction, protein restriction, reduced omega-3 consumption, and exposure to a high fat diet were associated with higher levels of offspring anxiety. Results were limited by a very large proportion of animal studies. High quality intervention studies involving human subjects are warranted to elucidate the precise dietary factors or constituents that modulate the risk of anxiety in offspring.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.519
Threshold uncertainty score0.789

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 head0.109
GPT teacher head0.402
Teacher spread0.293 · 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 designSystematic review
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

Citations8
Published2022
Admission routes1
Has abstractyes

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