How to establish, start and maintain an International Biomedical Research Consortium?
Bibliographic record
Abstract
Introduction Collaboration is crucial for innovative scientific contributions, often facilitated through consortia, which are particularly valuable for translating medical innovations to patient care. Despite the rise in consortia, there is limited literature available on how to establish a consortium. This paper aims to provide a comprehensive business and strategy plan for establishing and maintaining biomedical consortia. Methods We explored academic databases and used a snowball sampling method to expand our literature pool, including papers relevant to consortia design such as business and strategy plans. Thematic analysis identified recurring themes, and critical evaluations informed our recommendations, supplemented by experiences from establishing the Neuropsychiatric International Consortium of Frontotemporal Dementia (NIC-FTD). Discussion Our review and experience highlight three key elements for launching and maintaining a successful consortium: (1) Establishment steps and considerations, (2) Consortium agreement and strategy planning, and (3) Maintenance strategies. This paper offers practical guidance for scientists and physicians in establishing and maintaining effective consortia by providing a Consortium Strategy Plan. Conclusion This paper provides a detailed Consortium Strategy Plan with the aim to aid the successful establishment and maintenance of biomedical consortia and to support innovative scientific collaboration.
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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 | 0.193 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".