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Record W7129509058 · doi:10.5281/zenodo.18671784

Sustainability Plan (D8.18)

2024· article· W7129509058 on 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)

Bibliographic record

VenueOpen MIND · 2024
Typearticle
Language
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsnot available
FundersEuropean Commission
KeywordsSustainabilityStakeholderStakeholder engagementCorporate governanceThe InternetCollaborative networkPlan (archaeology)Product (mathematics)

Abstract

fetched live from 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.802
Threshold uncertainty score0.664

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0060.003
Open science0.0030.006
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.1980.076

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.

Opus teacher head0.081
GPT teacher head0.415
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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