PP-12 Mag – medicaments administres pendant la grossesse/drugs administered in pregnancy
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».