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Enregistrement W7129509058 · doi:10.5281/zenodo.18671784

Sustainability Plan (D8.18)

2024· article· W7129509058 sur OpenAlexaboutno aff
University Medical Center Utrecht, Uppsala University, KU Leuven, Netherlands Pharmacovigilance Centre Lareb, Newcastle upon Tyne Hospitals NHS Foundation Trust, Centre Hospitalier Universitaire de Toulouse, The Synergist, European Network of Teratology Information Services, University of Manchester, Novartis (Switzerland), Sanofi (France)

Notice bibliographique

RevueOpen MIND · 2024
Typearticle
Langue
DomaineMedicine
ThématiquePregnancy and Medication Impact
Établissements canadiensnon disponible
Organismes subventionnairesEuropean Commission
Mots-clésSustainabilityStakeholderStakeholder engagementCorporate governanceThe InternetCollaborative networkPlan (archaeology)Product (mathematics)

Résumé

récupéré en direct d'OpenAlex

The ConcePTION project, an initiative under the IMI2 program, focuses on improving safety information regarding medication use during pregnancy and lactation. As the project nears its conclusion in December 2024, a sustainability strategy has been developed to ensure its outcomes are sustained and expanded upon. The plan revolves around key workstreams addressing critical public health gaps. Seven different workstreams are described for potential sustainability beyond ConcePTION: 1. Pregnancy Study Network – Primary Data (LIFETIME): The LIFETIME cohort focuses on monitoring neurocognitive outcomes in infants exposed to medications. The Minimum Viable Product (MVP) includes a subscription model granting access to data for regulatory purposes. However, the MVP remains underdeveloped due to funding and site expansion challenges. Collaboration with industry partners, regulatory bodies, and academic institutions underpins its pathway toward sustainability. 2. Pregnancy Study Network – Secondary Data (ConcePTION+EHR): This workstream builds a network leveraging secondary data for complex post-market safety studies. Using tools and expertise from ConcePTION, the initiative has gained interest from existing research networks like VAC4EU, SIGMA and EU PE&PV. Funding opportunities via IHI and EMA are being explored, but governance formalization and operational efficiency remain hurdles. 3. Lactation Network (Milk4Baby): The network aims to develop infrastructure and methods to assess drug safety in lactation through studies involving breastmilk samples. The proposed Milk4Baby initiative seeks to scale small pilots into a broader ecosystem, addressing regulatory gaps and logistical challenges such as systematic sampling and distributed storage. 4. MUMS: MUMS is a multilingual, pan-European knowledge database providing evidence-based drug safety information for pregnant women. Despite stakeholder enthusiasm, challenges in quantifying impact, building trust, and securing funding threaten its sustainability. Efforts include expanding collaborations and seeking alternative funding. 5. Teratology E-Learning Course: A course aimed at increasing knowledge about medication safety during pregnancy and lactation among healthcare professionals and students. The MVP is operational but faces limitations in accessibility and funding. Partnerships are being explored to expand reach. 6. Sustaining the Network: ConcePTION united public and private stakeholders around the shared goal of improving safety information. However, an alliance was not established due to insufficient buy-in. The "10-in-10" initiatives outline a roadmap for continued impact, focusing on evidence generation, dissemination, and community building. The ConcePTION initiative faces several challenges that must be addressed to ensure the long-term sustainability of its outcomes. A primary obstacle is securing sustainable funding across its various workstreams. Many initiatives, such as the LIFETIME cohort and MUMS database, require significant financial resources to achieve their Minimum Viable Products (MVPs) and maintain operations. Limited buy-in from stakeholders further complicates efforts to establish sustainable funding models in some cases. Another key challenge is stakeholder engagement. Despite positive validation efforts, gaps in collaboration and commitment, hinder progress. For instance, the proposed public-private alliance was not established due to insufficient partner buy-in, emphasizing the difficulty of fostering collective commitment within a diverse consortium. Regulatory pathways also present complexities. Initiatives like the LIFETIME cohort and the Milk4Baby project require time-intensive and resource-heavy regulatory qualification processes. These barriers delay MVP realization and limit immediate impact. Additionally, operational delays, particularly in recruitment and demonstrator studies, further impede progress across multiple workstreams. Despite these challenges, ConcePTION presents significant opportunities. Emerging regulatory incentives highlight the increasing demand for robust safety data, creating a favorable environment for projects like Milk4Baby and MUMS to meet these needs. Existing infrastructure and tools developed under ConcePTION have been integrated into broader research networks, such as VAC4EU and SIGMA, providing a strong foundation for future expansion. Global collaborations with regulators and international organizations offer scalability potential, as demonstrated by partnerships with the FDA, Health Canada, and other global entities. These relationships enhance ConcePTION's credibility and broaden its impact. Additionally, the "10-in-10" initiatives provide a clear vision for galvanizing stakeholders, focusing on evidence generation, dissemination, and community building to drive systemic change in public health. By addressing these challenges and capitalizing on these opportunities, ConcePTION has the potential to significantly improve medication safety for pregnant and breastfeeding women, ensuring a lasting impact in the field of maternal and child health.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,802
Score d'incertitude au seuil0,664

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,011
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0020,001
Communication savante0,0060,003
Science ouverte0,0030,006
Intégrité de la recherche0,0050,003
Charge utile insuffisante (le modèle a refusé de juger)0,1980,076

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.

Tête enseignante Opus0,081
Tête enseignante GPT0,415
Écart entre enseignants0,334 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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