PP-12 Mag – medicaments administres pendant la grossesse/drugs administered in pregnancy
Bibliographic record
Abstract
Background Maternal drug use in pregnancy may oc-cur in different situations: chronic maternal disease prior to pregnancy, maternal disease not linked to pregnancy or complicating pregnancy and automedication. Studies in Europe and USA/Canada have shown high numbers of drugs used by pregnant women, up to 13 with prescrip-tion rates over 90% in France. This is a major healthcare issue for clinicians as more than 80% of the drugs used are used without knowledge of their safety/efficacy for the mother, have undetermined risks and possible adverse effects on the fetuses. In France, epidemiological data are insufficient to evaluate the drug use during preg-nancy and the status of the drugs prescribed (licensed/off-label). Methods MAG is a large multicenter and prospective study conducted using an electronic questionnaire. As a collaborative project, MAG Consortium includes clinical research units of APHP (CIC1426, CIC0901), the Gynecology and Obstetrics-CIC network (GO-CIC), the ‘Risks and Pregnancy’ University-Hospital department and INSERM U953 unit. The objectives are to determine the extent of drug use during pregnancy, determine drug status, conditions of use (prescription/automedication), and to identify per-sonal, social and economic factors conditioning their use in a representative population of 1000 randomly selected pregnant women in France. Therefore, France was divid-ed into 7 regions with 1 perinatal network selected per region. Using childbirths epidemiological data from the French National Institute of Statistics and Economic Stud-ies, a total of 35 maternity wards will participate: 5 units per region (1 level III, 2 level II, 2 level I) with 1 private unit to ensure the best representativeness of the results with recruitments established by region and by age groups. Seven mobile CRAs are in charge of the interviews using MAG electronic questionnaire facilitating the capture and real-time monitoring of the inclusions with a list of 350 most used drugs (in pregnant women) uploaded on the platform. MAG is conducted over a period of 5 days in each centre. Results To date, the 35 maternity wards and the perina-tal networks have been identified within the 7 regions: Yvelines (MYPA), Pays de la Loire (Sécurité Naissance), Basse-Normandie, Bourgogne (Femme et Enfant), Rhône-Alpes (Aurore) and Provence Alpes Côte d’Azur (Méditer-ranée). From June 2016 to mid-March 2017, the MAG network allowed to recruit 860 patients in 16 centres with 13 com-pleted weeks and 10 days of study conduct, a mean of 58 women per centre (13 centres) or 12 women includ-ed per day. MAG interviews are less than 30 min per woman. A refusal rate of 15% was observed reflecting that MAG was very well received among pregnant wom-en. The MAG survey is still ongoing with inclusions sched-uled until June 2017. Inclusions will be extended up to 2000 patients. Conclusion MAG study will deliver essential information of drug use in pregnant women identifying potential associated factors and determine drugs that would ne-cessitate complementary pharmacological studies. MAG will orientate information and communication strategies of health professionals and women to limit inappropriate drug exposures and provide the tools for future studies to be conducted through national surveillance networks.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".