[THE APPROACH TO NAUSEA AND VOMITING IN PREGNANCY].
Bibliographic record
Abstract
Nausea and vomiting of pregnancy (NVP) is the most prevalent medical condition during gestation. Approximately 85% of pregnant women suffer from some degree of this condition, while hyperemesis gravidarum (HG), the most severe form, affects up to 2% of women. Although being the leading cause for hospitalization during pregnancy, NVP has received little attention from the medical community. NVP negatively affects women's quality of life, household activity and work productivity. In Canada, the financial cost of NVP, ranges from $132 to $653 per woman/week. In extreme cases, severe NVP results in therapeutic abortions. On the other hand, NVP has been shown to have a protective effect against spontaneous abortions and congenital malformations. Lately, there has been an interest in the hypothesis that NVP is a mechanism protecting the fetus from phytochemicals. Early treatment can prevent future complications and deterioration of the symptoms. Various studies have demonstrated the effectiveness and safety of antiemetic therapy in pregnancy. However, fear of teratogenicity and lack of clinical guidelines lead to trial and error NVP management. We present an updated algorithm for the management of NVP.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".