The safety of drugs for the treatment of nausea and vomiting of pregnancy
Bibliographic record
Abstract
Nausea and vomiting of pregnancy (NVP) is the most common medical condition of pregnancy, affecting up to 80% of all pregnancies to some degree. In most cases it subsides by the week 16 of pregnancy, although up to 20% of women continue to have symptoms throughout pregnancy. Severe NVP (Hyperemesis gravidarum) affects < 1% of women and in some severe cases can require hospitalization and rehydration of fluids. Women suffer not only physically but also psychologically, which has been documented in a number of studies. In addition, some women have decided to terminate their pregnancy rather than tolerate severe symptoms. Even less severe cases of NVP can have significant adverse effects on the quality of a woman's life, affecting her occupational, social, domestic functioning and general well being. Therefore, it is of great importance to treat this condition effectively to improve the quality of life for these women. In this paper, the authors review different classes of antiemetics used to treat this condition and discuss that some have better safety profiles than others, but most appear to be safe to use in pregnancy. Also included is a treatment algorithm that can assist the healthcare provider in treating this condition in pregnant safely and effectively.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".