Development Studies on Treatment of Vomiting and Nausea: Bibliometrical Analysis from the Years 1946-2024
Bibliographic record
Abstract
Nausea is the most common thing in pregnancy that is a symptom of discomfort that occurs in the early stages of pregnancies. This event has a considerable physical, social and psychological impact on women. The emotional and physical impact includes feelings of anxiety about the possibility of affecting the fetus. Research on the implementation of vomiting nausea in pregnancy found as many as 3030 articles, taken from the Pubmed database between 1875 and 2024. The purpose of the bibliometric analysis is to find out the research trends, the most widely used keywords, the journal of the most publishers, the author's instance and the country of collaboration of the author of the article. The keywords most commonly used by the authors are human, female, pregnancy, hyperemesis gravidarum, vomiting, nausea, treatment outcome. The American Journal of Obstetrics and Gynecology ranks first as the most widely published journal on Treatment vomiting and nausea, with a total of 144 articles. The highest affiliation of authors is from the University of Amsterdam and the Université de Californie, which have a total of 75 authors in common. The University of Toronto is in the next position with 64 authors, followed by the university of Cambridge with 70 authors. Multiple Country Publications (MCP) and Single Country Publication (SCP) collaborations are from the United States, China and Canada.
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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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.153 | 0.218 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".