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Record W2074353918 · doi:10.1016/j.jmwh.2004.03.011

Determinants of Women's Decision Making on Whether to Treat Nausea and Vomiting of Pregnancy Pharmacologically

2004· article· en· W2074353918 on OpenAlexaffabout
Anne Baggley, Yvette Navioz, Caroline Maltepe, Gideon Koren, Adrienne Einarson

Bibliographic record

VenueJournal of Midwifery & Women s Health · 2004
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsNauseaVomitingMedicinePregnancyObstetricsMetoclopramideHyperemesis gravidarumDrugPediatricsGynecologyFamily medicineIntensive care medicineAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

Nausea and vomiting of pregnancy (NVP) affects up to 80% of all women to some degree during their pregnancies. Diclectin (doxylamine and pyridoxine [vitamin B6]) has been on the Canadian market for many years and is indicated as the drug of choice for the treatment of NVP. However, some women choose not to treat NVP with pharmacologic measures, perhaps due to a persistent fear of teratogenic risk. The objective of this study was to determine the factors that influence a woman's decision not to treat NVP with pharmacologic measures. Fifty-nine women recruited from the Motherisk Nausea and Vomiting Helpline completed a questionnaire. All were informed that Diclectin was considered safe for use during pregnancy. At a follow-up telephone call, 34% were not using any pharmacologic treatment, and of those who were taking the drug, 26% were using less than the recommended dose. Reasons cited for not using the medication were insufficient safety data, preference for non-pharmacologic methods, and being made to feel uncomfortable by the physician. Of the women who did use Diclectin, the most convincing reassuring information that it was safe to use came from friends and family. Many other factors play a large role in a women's decision making.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.031
GPT teacher head0.387
Teacher spread0.355 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations47
Published2004
Admission routes2
Has abstractyes

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