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Record W2182380244 · doi:10.1186/s12884-015-0746-2

Treatment of nausea in pregnancy: a cross-sectional multinational web-based study of pregnant women and new mothers

2015· article· en· W2182380244 on OpenAlexaff
Kristine Heitmann, Lone Holst, Angela Lupattelli, Caroline Maltepe, Hedvig Nordeng

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2015
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersNorges Forskningsråd
KeywordsNauseaPregnancyMedicineReproductive medicineCross-sectional studyVomitingObstetricsFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The factors related to the treatment of nausea during pregnancy have not yet been investigated in several countries simultaneously. The present study aimed to describe differences in self-reported nausea during pregnancy and the patterns of use for both conventional and herbal medicines across countries. The factors related to nausea and its treatment and the relationships between different self-reported co-morbidities and nausea were also investigated. METHODS: This cross-sectional study used data collected by a web-based questionnaire distributed between October 2011 and February 2012 in several countries within five regions: Western, Northern, and Eastern Europe, North America, and Australia. Women who were pregnant or had a child less than one year old were eligible to participate. RESULTS: A total of 9113 women were included in the study, whereof 6701 (73.5%) had experienced nausea during pregnancy. Among respondents with nausea, conventional medicines were used by 1201 (17.9%) women and herbal medicines by 556 (8.3%) women. The extent of self-reported nausea and its treatment varied by country. Education, working status, and folic acid use were significantly associated with the use of conventional medicines against nausea. Respondents who had nausea also had a high burden of co-morbidity. CONCLUSION: The prevalence of nausea was high across all participating countries but its treatment varied, possibly due to cultural differences and differences in attitudes towards medicines. A high degree of co-morbidity was found among respondents with nausea.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.052
GPT teacher head0.323
Teacher spread0.271 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

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