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Enregistrement W3094621708 · doi:10.1016/s2589-7500(20)30242-9

The International Hundred Thousand Plus Cohort Consortium: integrating large-scale cohorts to address global scientific challenges

2020· article· en· W3094621708 sur OpenAlex

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Notice bibliographique

RevueThe Lancet Digital Health · 2020
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueHealth, Environment, Cognitive Aging
Établissements canadiensOntario GenomicsOntario Institute for Cancer Research
Organismes subventionnairesNational Institutes of HealthWellcome Trust
Mots-clésCohortCharterCohort studyGlobal healthScale (ratio)Library scienceAllianceScopusMedicineGeographyPolitical scienceHealth careGerontologyMEDLINEComputer science

Résumé

récupéré en direct d'OpenAlex

Large cohort studies involving hundreds of thousands of participants have been established or launched in several regions worldwide. Cohorts provide great value for studying diverse populations and key demographic subgroups, rare genotypes and exposures, and gene-environment interactions.1Lewington S Clarke R Qizilbash N Peto R Collins R Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies.Lancet. 2002; 360: 1903-1913Summary Full Text Full Text PDF PubMed Scopus (7927) Google Scholar Each cohort is constrained, however, by its size, ancestral origins, and geographical boundaries, which limit the subgroups, exposures, outcomes, and interactions it can examine. Linking data across large cohorts provides a vast digital resource of diverse data to address questions that none of these cohorts can answer alone, enhancing the value of each cohort and leveraging the enormous investments made in them to date. Leaders of large-scale cohorts, with support from the National Institutes of Health and the Wellcome Trust, and in collaboration with the Global Alliance for Genomics and Health (GA4GH) and the Global Genomic Medicine Collaborative (G2MC), have come together to form the International Hundred Thousand Plus Cohort Consortium (IHCC). As of May, 2020, IHCC comprises 103 cohorts in 43 countries involving nearly 50 million participants (figure, appendix). Collaborative efforts to date have focused on developing a queryable cohort registry and data sharing platform, identifying and piloting high-priority scientific projects, and developing a charter and governance structure to foster collaborations. IHCC members generally meet five criteria: greater than 100 000 enrolled participants; longitudinal follow-up in place for health outcomes; selection not based on a specific disease; biological samples collected from participants; and leaders willing and able to share data or metadata with IHCC members. Cohorts with less than 100 000 participants can apply to become full members if they include low-income and middle-income countries or disadvantaged populations in high-income countries, or if they collect data from exceptional or hard-to-accrue groups. Membership is granted by a majority vote of the Scientific Steering Committee, comprising 15 cohort leaders elected from and by the IHCC membership and representing the diversity of the IHCC. Cohorts not meeting all of the criteria for full membership, as well as people with specific expertise who do not bring a cohort into the IHCC, are eligible for affiliate membership. Industry representatives are eligible if they meet criteria for full or affiliate membership but must abide by IHCC guidelines for collaboration with industry and do not have voting rights. Policies have also been established for data sharing and collaborative publications. An important first step in facilitating collaborations is to develop a standardised atlas or registry to share basic descriptive information about each cohort, to enhance the international visibility and engagement of the cohorts and to which cohort leaders could direct the myriad such enquiries they receive. IHCC's Data and Infrastructure Team has developed a prototype resource allowing investigators to identify IHCC member cohorts' key characteristics and standardise or harmonise key metadata elements to promote interoperability. Building on existing standards and infrastructure such as the GA4GH and Maelstrom projects,2Global Alliance for Genomics and HealthGENOMICS. A federated ecosystem for sharing genomic, clinical data.Science. 2016; 352: 1278-1280Crossref PubMed Scopus (144) Google Scholar, 3Bergeron J Doiron D Marcon Y Ferretti V Fortier I Fostering population-based cohort data discovery: The Maelstrom Research cataloguing toolkit.PLoS One. 2018; 13e0200926Crossref PubMed Scopus (27) Google Scholar the atlas is designed around use cases such as finding cohorts with specific measurements or particular demographic subgroups. Next steps will involve building semi-automated tools to import data dictionaries and map similar variables across datasets. Regulatory barriers to sharing of individual participant data in many countries might necessitate some analyses being done within cohorts in a federated model,4Knoppers BM Framework for responsible sharing of genomic and health-related data.HUGO J. 2014; 8: 3Crossref PubMed Scopus (113) Google Scholar, 5Contreras JL Reichman JH Sharing by design: data and decentralised commons: overcoming legal and policy obstacles.Science. 2015; 350: 1312-1314Crossref PubMed Scopus (37) Google Scholar producing summary data to be shared for meta-analyses. IHCC's Scientific Strategies Team is soliciting ideas for collaborative scientific projects from the IHCC membership, giving priority to projects involving innovative use of existing resources, broad scope across numerous cohorts, so-called quick wins within a finite timescale, and opportunities for the career development of junior researchers. A proof-of-principle scientific project involves development and testing of polygenic risk scores in four complex traits across four broad ancestry groups in seven cohorts to show the speed and robustness of this approach. With the advent of the COVID-19 pandemic, plans are underway to implement standardised collection of data and specimens to identify predictors of susceptibility to and severity of SARS-CoV2 infection as well as psychological and economic effects of the pandemic, particularly in the low-income and middle-income countries that are well-represented in the IHCC.6Abbott A Thousands of people will help scientists to track the long-term health effects of the coronavirus crisis.Nature. 2020; 582: 326Crossref PubMed Scopus (4) Google Scholar Leaders of other large cohorts are invited to join the IHCC by contacting [email protected]. Cohort leaders are encouraged to participate in IHCC teams and annual international summits and to share their data in ways consistent with participants' consent and local regulations. IHCC views cohort independence and individuality as major strengths and is committed to ensuring that cohorts in low-income settings have sufficient resources to participate actively while maintaining their own sovereignty. Please join us! GG reports being founder of the Global Genomic Medicine Collaborative, an independent not-for-profit 501(c)3 non-profit organisation. The authors express their appreciation for the valuable assistance of the IHCC Secretariat, and particularly Eric Plummer, Teji Rakhra-Burris, and Meredith Towery, in preparing this manuscript. We would also like to recognise the efforts of all on the IHCC Steering Committee, the teams, cohort leaders, and membership in bringing the IHCC to fruition. Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations. Download .xlsx (.04 MB) Help with xlsx files Supplementary appendix 1

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,515
Score d'incertitude au seuil0,935

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,042
Tête enseignante GPT0,307
Écart entre enseignants0,266 · 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