The Leading Concerns of American Women with Nausea and Vomiting of Pregnancy Calling Motherisk NVP Helpline
Bibliographic record
Abstract
Background. Nausea and vomiting of pregnancy (NVP) is the most common medical condition of pregnancy, affecting up to 85% of expecting mothers. In the USA, there is no FDA-approved medication for the treatment of NVP. Objective. To identify the primary concerns of American women leading them to contact the Motherisk NVP Helpline and to characterize the severity of their symptoms and therapy offered in order to develop improved and customized counseling for them. Methods. We reviewed the intake forms of the American women who called the NVP Helpline from 2008 to 2012. We extracted their state of residence, demographic data, severity of NVP symptoms, and other available clinical characteristics. Results. A total of 195 forms were reviewed. Of these, 86% called for information on management of NVP with/without questions about fetal drug safety, while 14% called solely about drug safety during pregnancy/breastfeeding. The majority of women were Caucasian, in their thirties, educated, employed, married and in their second pregnancy. Of them 95% were suffering from moderate-to-severe condition with 13% having hyperemesis gravidarum. Conclusion. American women need more information on the management of NVP and on a variety of its aspects in addition to the safety and effectiveness of antiemetic medications. Their leading concern was the use of doxylamine and vitamin B6 combination for NVP treatment followed by the use of ondansetron.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".