Bibliographic record
Abstract
Nausea and vomiting of pregnancy (NVP) is a common medical condition in pregnancy with significant physical and psychological morbidity. Up to 90% of women will suffer from NVP symptoms in the first trimester of pregnancy with up to 2% developing hyperemesis gravidarum which is NVP at its worst, leading to hospitalization and even death in extreme cases. Optimal management of NVP begins with nonpharmacological approaches, use of ginger, acupressure, vitamin B6, and dietary adjustments. The positive impact of these noninvasive, inexpensive and safe methods has been demonstrated. Pharmacological treatments are available with varying effectiveness; however, the only drug marketed specifically for the treatment of NVP in pregnancy is Diclectin(®) (vitamin B6 and doxylamine). In addition, the Motherisk algorithm provides a guideline for use of safe and effective drugs for the treatment of NVP. Optimal medical management of symptoms will ensure the mental and physical wellbeing of expecting mothers and their developing babies during this often stressful and difficult time period. Dismissing NVP as an inconsequential part of pregnancy can have serious ramifications for both mother and baby.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".