Determinants of Adherence to Delayed-Release Doxylamine and Pyridoxine in Patients With Nausea and Vomiting of Pregnancy
Bibliographic record
Abstract
OBJECTIVE: Women often hesitate to take medications in pregnancy due to fears of perceived potential fetal damage. The authors' objective is to identify the determinants of adherence to delayed-release doxylamine-pyridoxine (Diclectin) in patients with nausea and vomiting of pregnancy (NVP). METHODS: The authors performed a prespecified secondary analysis of a multicenter double-blind randomized controlled trial of Diclectin versus placebo for the treatment of NVP. Data on adherence to study medication were collected in all patients. The primary outcome of this analysis was adherence to study medication, which was determined by pill counting and patient diaries. The treatment regimen in the original trial was not fixed and depended on patient's symptoms. There was no difference in the adherence rates between subjects in the Diclectin or placebo arms of the study, so the 2 arms were analyzed as one cohort. The degree of adherence was analyzed in the various subgroups. Subsequently, a multiple linear regression model was constructed to identify predictors to adherence. RESULTS: Two hundred fifty-eight women were included in this analysis. There were no differences in adherence rates according to ethnicity, race, or the presence of adverse events. Gravidity, average number of prescribed tablets per day, site of enrollment, and change in NVP severity measured by the pregnancy unique-quantification of emesis score were associated with adherence. In multivariable analysis, average number of tablets per day, change in pregnancy unique-quantification of emesis, number of treatment days, site of enrollment were significantly predictive of adherence, with the former being negatively correlated. CONCLUSION: Adherence to antinauseants for NVP is affected by number of tablets prescribed per day, and treatment duration and effectiveness.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".