Dysmenorrhea as a Risk Factor for Hyperemesis Gravidarum
Bibliographic record
Abstract
Background: The aims of the study were to assess the association of dysmenorrhea and hyperemesis gravidarum (HG) and to determine other factors that may influence its onset and severity. Methods: This is a prospective case-control IRB approved study of 344 consecutive singleton pregnant women with and without hyperemesis gravidarum in pregnancy from February 2011 to April 2012. The association between HG and dysmenorrhea in adolescent and adult was examined using Pearson’s Chi-square with Yates correlation, Student’s t -test and Mann-Whitney U test. Bivariate analysis and odd ratios (ORs) were calculated to evaluate the strength of their association, and multivariate logistic regression analysis while correcting for confounders. P-value of 0.05 was considered statistically significant. Results: A total of 344 consecutive singleton pregnant women were recruited. Significant association was found between HG and adolescent dysmenorrhea: 77.8% versus 43.4% of controls (P < 0.0001, OR: 4.6, 95% CI: 2.3 - 8.9). Also, there was a significant association between HG and adult dysmenorrhea: 76.4% versus 38.1% controls (P < 0.0001, OR: 5.3, 95% CI: 2.7 - 10.2). The association of severe adolescent and adult dysmenorrhea with HG was stronger (P < 0.0001, OR: 8.8, 95% CI: 3.9 - 19.9 and P < 0.0001, OR: 12.2, 95% CI: 5.0 - 29.7 respectively). There was a modest association with moderate dysmenorrhea (P = 0.004, OR: 3.1, 95% CI: 1.4 - 6.9) which was not sustained but no associations were found between HG and all mild dysmenorrhea of both adolescent and adult. Conclusion: This study found an association between adolescent and adult dysmenorrhea and HG. These associations were stronger with severe dysmenorrhea. J Clin Gynecol Obstet. 2015;4(4):283-289 doi: http://dx.doi.org/10.14740/jcgo356w
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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 | 0.002 |
| 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) | 0.002 | 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".