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Record W3000598319 · doi:10.2196/15646

EXamining ouTcomEs in chroNic Disease in the 45 and Up Study (the EXTEND45 Study): Protocol for an Australian Linked Cohort Study

2020· article· en· W3000598319 on OpenAlexvenueno aff
Celine Foote, Carinna Hockham, Louisa Sukkar, Anna Campain, Amy Kang, T. Kue Young, Alan Cass, Clara K Chow, Elizabeth Comino, Martin Gallagher, Stephen Jan, John Knight, Bette Liu, Martin McNamara, David Peiris, Carol A. Pollock, David Sullivan, Germaine Wong, Sophia Zoungas, Kris Rogers, Min Jun, Meg Jardine

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsnot available
FundersNew South Wales GovernmentNSW Ministry of HealthEli Lilly and CompanyNational Heart Foundation of AustraliaCancer Council NSWAmgen
KeywordsMedicinePopulationCohortDiseaseFamily medicineCohort studyKidney diseaseDisease managementGerontologyEnvironmental healthPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic kidney disease (CKD) and diabetes are the major causes of death and disability worldwide. They are associated with high health service utilization persisting over many years. Their slow progression and wide clinical variation make them eminently suitable for study in population-based cohorts. However, current understanding of their prevalence, incidence, and progression is largely based on studies conducted in clinical populations. OBJECTIVE: This study aims to establish a novel link between an existing population-based cohort (the 45 and Up Study) and routinely collected laboratory and administrative data to facilitate research across the full disease spectrum of CKD and diabetes. METHODS: In the EXTEND45 Study (EXamining OuTcomEs in chroNic Disease in the 45 and Up Study), baseline questionnaire responses of over 260,000 participants of the 45 and Up Study aged ≥45 years living in New South Wales (NSW), collected between January 2006 and December 2009, are linked to data from laboratory service providers as well as national- and state-based administrative datasets via probabilistic linkage. Routinely collected data were obtained for participants who could be linked between January 2005 and July 2013. Laboratory data will enable the identification of early cases of chronic disease and the assessment of clinically relevant biochemical targets during the disease course. Health administrative datasets will allow for the examination of health service use, pharmacological management, and clinical outcomes. RESULTS: The study received ethics approval from the NSW Population and Health Services Research Ethics Committee in February 2014. Data linkage for 267,153 of the 45 and Up Study participants was completed in June 2016, with congruent linkage achieved for 265,086 (99.23%) individuals. To date, the CKD and diabetes cohorts have been identified (published elsewhere), and a diverse portfolio of research projects relating to disease burden, risk factors, health outcomes, and health service utilization is in development. CONCLUSIONS: The EXTEND45 Study represents an unparalleled opportunity to perform extensive research into diseases of considerable public health and clinical importance. Strengths include the population-based nature of the cohort and the availability of longitudinal information on the complete disease pathway for affected individuals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/15646.

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)0.047
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.047
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.033
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.004
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0240.007

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 head0.471
GPT teacher head0.593
Teacher spread0.122 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

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

Citations11
Published2020
Admission routes1
Has abstractyes

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