Effectiveness of the Additional Risk Minimisation Measures for Valproate Among Healthcare Professionals and Patients: A Cross‐Sectional Survey in Six European Countries
Bibliographic record
Abstract
PURPOSE: To assess the impact of the 2018 European additional risk minimisation measures (aRMMs) regarding the use of valproate in women of childbearing potential (WCBP) and during pregnancy. METHODS: A cross-sectional, non-interventional survey conducted in six European countries among 1982 healthcare professionals (HCPs) (July-October 2020) and 779 WCBP treated with valproate for epilepsy, bipolar disorder or other indications (August 2020-February 2021). HCPs were prescribing physicians (neurologists, psychiatrists, paediatricians and GPs), gynaecologists and pharmacists. Prespecified criteria were defined for success in the dimensions of awareness, knowledge and behaviour (correct answers to ≥ 80% of questions at individual level) and overall success (≥ 90%/80% successful HCPs/patients respectively, in the behaviour dimension and one of the two other dimensions). RESULTS: HCPs and patients did not meet the success criteria either overall or in any dimension. Highest success rates were in the behaviour dimension for gynaecologists (71.7%), pharmacists (49.7%) and patients (51.2%), and in the awareness dimension for prescribing physicians (23.6%). HCPs reported being unfamiliar with some educational materials and lacked knowledge of detailed prescribing conditions for valproate and the need for contraception regardless of sexual activity. More than 50% of patients were aware of the relevant patient materials and knew about the teratogenic risks of valproate. CONCLUSION: Self-reported levels of awareness, knowledge and behaviour varied considerably by HCP type and among patient respondents. Further investigation is needed into why certain measures of the pregnancy prevention programme are not well known and followed, to improve their effectiveness. This will be addressed in a qualitative study which will be based on interviews with HCPs and patients.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".