Epidemiology of nausea and vomiting of pregnancy: prevalence, severity, determinants, and the importance of race/ethnicity
Bibliographic record
Abstract
BACKGROUND: Studies that contributed to the epidemiology of nausea and vomiting of pregnancy have reported conflicting findings, and often failed to account for all possible co-variables necessary to evaluate the multidimensional associations. The objectives of this study were to: 1) Estimate the prevalence and the severity of nausea and vomiting of pregnancy during the 1st and the 2nd trimester of pregnancy, and 2) Identify determinants of presence and severity of nausea and vomiting of pregnancy during the 1st and 2nd trimesters separately, with a special emphasis on the impact of race/ethnicity. METHODS: A prospective study including pregnant women attending the Centre Hospitalier Universitaire (CHU) Sainte-Justine or René-Laennec clinics for their prenatal care was conducted from 2004 to 2006. Women were eligible if they were > or = 18 years of age, and </= 16 weeks of gestation. Women were asked to fill out a 1st trimester self-administered questionnaire and were interviewed over the telephone during their 2nd trimester of pregnancy. Presence of nausea and vomiting of pregnancy was based on the reporting of pregnant women (yes/no); severity of symptoms was measured by the validated modified-PUQE index. RESULTS: Of the 367 women included in the study, 81.2% were Caucasians, 10.1% Blacks, 4.6% Hispanics, and 4.1% Asians. Multivariate analyses showed that race/ethnicity was significantly associated with a decreased likelihood of reporting nausea and vomiting of pregnancy (Asians vs. Caucasians OR: 0.13; 95%CI 0.02-0.73; and Blacks vs. Caucasians OR: 0.29; 95%CI 0.09-0.99). CONCLUSION: Our study showed that race/ethnicity was associated with the reporting of nausea and vomiting of pregnancy in the 1st trimester of pregnancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".