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Record 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 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Lancet Digital Health · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsOntario GenomicsOntario Institute for Cancer Research
FundersNational Institutes of HealthWellcome Trust
KeywordsCohortCharterCohort studyGlobal healthScale (ratio)Library scienceAllianceScopusMedicineGeographyPolitical scienceHealth careGerontologyMEDLINEComputer science

Abstract

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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

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.042
GPT teacher head0.307
Teacher spread0.266 · 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