Patient decision aids for antidepressant use in pregnancy: a pilot randomised controlled trial in the UK
Bibliographic record
Abstract
BACKGROUND: Decision-making regarding antidepressant use in pregnancy is challenging, given the uncertain evidence base on the benefits and risks for women and their children. Patient decision aids (PDAs) can improve shared decision-making for complex health decisions but no evidence-based PDAs exist for antidepressant use in pregnancy. AIM: To assess the feasibility of a full-scale randomised controlled trial (RCT) to evaluate the efficacy of an electronic PDA on antidepressant use in pregnancy. DESIGN & SETTING: A UK-based pilot parallel-group RCT. METHOD: The study recruited women whose clinicians recommended an antidepressant for depression in a current or planned pregnancy, and who were uncertain about antidepressant use while pregnant. Women were recruited via clinician or self-referral, and randomised to online access to the PDA or online access to standard resource list, with primary follow-up at 4 weeks and longer-term follow-up. The primary outcome was protocol feasibility (recruitment target of 50 women and follow-up rate of 80%). Outcome measures for a future full-scale RCT included the decisional conflict scale (DCS). RESULTS: Fifty-one women were recruited with a follow-up rate of 90.2% at 4 weeks. The PDA received good overall satisfaction ratings (mean 4.2/5). Analysis of covariance (ANCOVA) indicated a small improvement in decisional conflict at 4 weeks, accounting for baseline scores (DCS regression coefficient = -3.5, 95% confidence intervals [CI = -12.6 to 5.6]). CONCLUSION: This pilot RCT for an electronic PDA on antidepressant use in pregnancy showed that the study protocol was feasible, with high rates of participant satisfaction among those randomised to the PDA.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".