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Biomarkers of Perinatal Psychopathology

2015· book· en· W1764471427 on OpenAlexaff
Simone N. Vigod, Meir Steiner

Bibliographic record

VenueOxford University Press eBooks · 2015
Typebook
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsPsychopathologyPsychosocialPsychiatryPregnancyMedicineStressorPostpartum periodPsychology

Abstract

fetched live from OpenAlex

Much research has focused on understanding why women are at increased risk of serious mental health symptoms during pregnancy and the postpartum. Although psychosocial stressors play a major role in perinatal psychiatric disorders, not every woman who experiences adverse psychosocial circumstances develops a major psychiatric illness during this time. As such, attention has focused on exploring how biological factors might impact the development of perinatal psychopathology. This chapter reviews biological changes during pregnancy and the postpartum that may contribute to the onset and/or exacerbation of psychiatric symptoms and disorders in the perinatal period. It discusses heritability and genetics research suggesting that some women may have a biological predisposition to developing psychopathology in the perinatal period. Then, the chapter focuses on pregnancy- and childbirth-related biological changes in sex hormones; the neurotransmitter, endocrine, and immune systems; and sleep that may be contributing biological factors in perinatal psychopathology for women at risk.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.033
GPT teacher head0.264
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

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