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Record W2997420438 · doi:10.36811/ojgor.2019.110010

Antipsychotics during Pregnancy: Pros and Cons

2019· article· en· W2997420438 on OpenAlexaff
Mary V. Seeman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPregnancyDiscontinuationMedicineAntipsychoticPsychiatrySchizophrenia (object-oriented programming)Adverse effectconsFamily medicinePsychologyObstetricsPharmacologyComputer science

Abstract

fetched live from OpenAlex

Background: As a general rule, medical professionals agree that it is best to avoid all drugs during pregnancy. Sometimes, however, drugs are essential to a woman’s health and well-being and to the safety of her fetus. Aim: The aim of this article is to review the pros and cons of pregnant women with schizophrenia remaining on antipsychotic medication. Method: The medical database, PubMed, was initially searched for literature in English of the last 5 years using the search terms: “pregnancy” and “antipsychotics”. Forty-four papers were selected. Results: There is no easy answer to the question of the wisdom of continuing antipsychotics during pregnancy. The reviewed literature suggests that the decision depends on the woman’s previous experience, the severity of her illness, her stage of pregnancy, and the specifics of the drug she is taking. Conclusion: As long as the woman is well-informed and competent to make decisions, she needs to carefully weigh benefits against risks to make the final determination. Whatever the decision, close clinical monitoring is warranted throughout pregnancy and the postpartum period. Keywords: Schizophrenia; Adverse Effects; Antipsychotic Discontinuation; Pregnancy

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)0.004
metaresearch head score (Gemma)0.020
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: Commentary · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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
GenreCommentary

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

Citations2
Published2019
Admission routes1
Has abstractyes

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