The Effect of Heartburn and Acid Reflux on the Severity of Nausea and Vomiting of Pregnancy
Bibliographic record
Abstract
BACKGROUND: Heartburn (HB) and acid reflux (RF) in the nonpregnant population can cause nausea and vomiting; therefore, it is plausible that in women with nausea and vomiting of pregnancy (NVP), HB/RF may increase the severity of symptoms. OBJECTIVE: To determine whether HB/RF during pregnancy contribute to increased severity of NVP. METHODS: A prospectively collected cohort of women who were experiencing NVP and HB, RF or both (n=194) was studied. The Pregnancy-Unique Quantification of Emesis and Nausea (PUQE) scale and its Well-being scale was used to compare the severity of the study cohort's symptoms. This cohort was compared with a group of women experiencing NVP but no HB/RF (n=188). Multiple linear regression was used to control for the effects of confounding factors. RESULTS: Women with HB/RF reported higher PUQE scores (9.6+/-2.6) compared with controls (8.9+/-2.6) (P=0.02). Similarly, Well-being scores for women experiencing HB/RF were lower (4.3+/-2.1) compared with controls (4.9+/-2.0) (P=0.01). Multiple linear regression analysis demonstrated that increased PUQE scores (P=0.003) and decreased Well-being scores (P=0.005) were due to the presence of HB/RF as opposed to confounding factors such as pre-existing gastrointestinal conditions/symptoms, hyperemesis gravidarum in previous pregnancies and comorbidities. CONCLUSION: The present cohort study is the first to demonstrate that HB/RF are associated with increased severity of NVP. Managing HB/RF may improve the severity 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".