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Record W2910676512 · doi:10.1080/03009734.2018.1556754

Rationale for a Swedish cohort consortium

2019· article· en· W2910676512 on OpenAlexaff
Johan Sundström, Cecilia Björkelund, Vilmantas Giedraitis, Per-Olof Hansson, Marieann Högman, Christer Janson, Ilona Koupil, Margareta Kristenson, Ylva Trolle Lagerros, Jerzy Leppert, Lars Lind, Lauren Lissner, Ingegerd Johansson, Jonas F. Ludvigsson, Peter M. Nilsson, Håkan Olsson, Nancy L. Pedersen, Andreas Rosenblad, Annika Rosengren, Sven Sandin, Tomas Snäckerström, Magnus Stenbeck, Stefan Söderberg, Elisabete Weiderpass, Anders Wanhainen, Patrik Wennberg, Isabel Fortier, Susanne Heller, Maria Storgärds, Bodil Svennblad

Bibliographic record

VenueUpsala Journal of Medical Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsMcGill University Health Centre
FundersAkademiska Sjukhuset
KeywordsMedicineEpidemiologyCohortCohort studyCompetence (human resources)GerontologyFamily medicinePathologyManagement

Abstract

fetched live from OpenAlex

We herein outline the rationale for a Swedish cohort consortium, aiming to facilitate greater use of Swedish cohorts for world-class research. Coordination of all Swedish prospective population-based cohorts in a common infrastructure would enable more precise research findings and facilitate research on rare exposures and outcomes, leading to better utilization of study participants' data, better return of funders' investments, and higher benefit to patients and populations. We motivate the proposed infrastructure partly by lessons learned from a pilot study encompassing data from 21 cohorts. We envisage a standing Swedish cohort consortium that would drive development of epidemiological research methods and strengthen the Swedish as well as international epidemiological competence, community, and competitiveness.

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)0.287
metaresearch head score (Gemma)0.240
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.287
Threshold uncertainty score0.880

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2870.240
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0070.005
Scholarly communication0.0070.005
Open science0.0060.013
Research integrity0.0110.009
Insufficient payload (model declined to judge)0.0100.005

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 head0.026
GPT teacher head0.310
Teacher spread0.283 · 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 designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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