MétaCan
Menu
Back to cohort
Record W2906301457 · doi:10.1111/jne.12674

Prenatal programming of stress responsiveness and behaviours: Progress and perspectives

2018· review· en· W2906301457 on OpenAlexafffund
Hirotaka Hamada, Stephen G. Matthews

Bibliographic record

VenueJournal of Neuroendocrinology · 2018
Typereview
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsOffspringTransgenerational epigeneticsEpigeneticsDNA methylationFetal programmingDevelopmental psychologyPrenatal stressPregnancyPsychologyEpigenesisBiologyMedicineGenetics

Abstract

fetched live from OpenAlex

Parental exposure to stress or glucocorticoids either before or during pregnancy can have profound influences on neurodevelopment, neuroendocrine function and behaviours in offspring. Specific outcomes are dependent on the nature, intensity and timing of the exposure, as well as species, sex and age of the subject. Most recently, it has become evident that outcomes are not confined to first-generation offspring and that there may be intergenerational and transgenerational transmission of effects. There has been intense focus on the mechanisms by which such early exposure leads to long-term and potential transgenerational outcomes, and there is strong emerging evidence that epigenetic processes (histone modifications, DNA methylation, and small non-coding RNAs) are involved. New knowledge in this area may allow the development of interventions that can prevent, ameliorate or reverse the long-term negative outcomes associated with exposure to early adversity. This review will focus on the latest research, bridging human and pre-clinical studies, and will highlight some of the limitations, challenges and gaps that exist in the field.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.072
GPT teacher head0.379
Teacher spread0.307 · 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 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

Citations56
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of NeuroendocrinologySame topicBirth, Development, and HealthFrench-language works237,207