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Record W3043409543 · doi:10.2196/20160

Chronic Kidney Disease in Tasmania: Protocol for a Data Linkage Study

2020· article· en· W3043409543 on OpenAlexvenueno aff
Timothy Saunder, Alex Kitsos, Jan Radford, Kim Jose, Charlotte McKercher, R. Raj, Nadine Wiggins, Brian Stokes, Matthew Jose

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsnot available
FundersNational Coronial Information SystemTasmanian Community Fund
KeywordsKidney diseaseProtocol (science)Linkage (software)MedicineAlternative medicineGeneticsInternal medicineBiologyPathology

Abstract

fetched live from OpenAlex

Background Chronic kidney disease (CKD) is a significant and growing health burden globally. Tasmania has the highest state prevalence for non-Indigenous Australians and it has consistently had the lowest incidence and prevalence of dialysis in Australia. Objective To examine the gap between the high community prevalence of CKD in Tasmania and the low use of dialysis. Methods This is a retrospective cohort study using linked data from 5 health and 2 pathology data sets from the island state of Tasmania, Australia. The study population consists of any person (all ages including children) who had a blood measurement of creatinine with the included pathology providers between January 1, 2004, and December 31, 2017. This study population (N=460,737) includes within it a CKD cohort, which was detected via pathology or documentation of kidney replacement therapy (KRT; dialysis or kidney transplant). Kidney function (estimated glomerular filtration rate [eGFR]) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. Individuals with 2 measures of eGFR<60 mL/min/1.73 m2, at least 90 days apart, were identified as having CKD and were included in the CKD cohort. Individuals treated with dialysis or transplant were identified from the Australia and New Zealand Dialysis and Transplant Registry. Results The study population consisted of 460,737 people (n=245,573 [53.30%] female, mean age 47.4 years) who were Tasmanian residents aged 18 years and older and were followed for a median of 7.8 years. During the later 5 years of the study period, 86.79% (355,622/409,729) of Tasmanian adults were represented. The CKD cohort consisted of 56,438 people (ie, 12.25% of the study population; 53.87% (30,405/56,438) female, mean age 69.9 years) followed for a median of 10.4 years with 56,039 detected via eGFR and 399 people detected via documentation of KRT. Approximately half (227,433/460,737, 49.36%) of the study population and the majority of the CKD cohort (41,448/56,438, 73.44%) had an admission episode. Of the 55,366 deaths recorded in the study population, 45.10% (24,970/55,366) had CKD. Conclusions Whole-of-population approaches to examine CKD in the community can be achieved by data linkage. Over this 14-year period, CKD affected 12.25% (56,438/460,737) of Tasmanian adult residents and was present in 45.10% (24,970/55,366) of deaths. International Registered Report Identifier (IRRID) DERR1-10.2196/20160

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)0.046
metaresearch head score (Gemma)0.056
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 score0.051
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.056
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.007
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0510.007

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 head0.444
GPT teacher head0.589
Teacher spread0.145 · 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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