MétaCan
Menu
Back to cohort
Record W1499341391 · doi:10.1186/1471-2393-2-5

Discordance between physical symptoms versus perception of severity by women with nausea and vomiting in pregnancy (NVP)

2002· article· en· W1499341391 on OpenAlexafffund
Kiran Chandra, Laura A. Magee, Gideon Koren

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2002
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHospital for Sick Children
KeywordsRetchingNauseaVomitingPregnancyMedicineReproductive medicinePsychosocialFeelingSeverity of illnessAnesthesiaInternal medicineObstetricsPsychiatryPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Nausea and vomiting in pregnancy (NVP) is a multifaceted condition that affects more than half of pregnant women and can range in severity from mild nausea to severe dehydration. Presently physicians evaluate mostly physical symptoms of NVP in trying to assess the severity of the condition. The objective of this study was to investigate how factors, other than the physical morbidity of nausea and vomiting, influence self-perception of NVP by affected women. METHODS: Five hundred women with NVP calling a 1-800 NVP Healthline were asked to rate their NVP severity and report their nausea duration and number of vomiting/retching episodes. RESULTS: Nausea and vomiting/retching correlated significantly but very poorly with self-assessment of NVP severity. There was also a correlation between nausea duration and vomiting/retching frequency however the correlations were weak and overall physical symptoms could only explain 14% of the variability of women's feelings and perceptions through multivariate analysis. CONCLUSIONS: Physical symptoms weakly correlate with self-assessment of NVP severity. Other aspects of this condition, most probably psychosocial, influence women's perception of NVP severity.

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 score0.116
Threshold uncertainty score0.770

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 head0.017
GPT teacher head0.258
Teacher spread0.240 · 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

Citations19
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueBMC Pregnancy and ChildbirthSame topicPregnancy and Medication ImpactFrench-language works237,207