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Record W4397043219 · doi:10.1681/asn.20233411s1373a

The Prevalence of CKD in Australian Primary Care: Analysis of a National General Practice Dataset

2023· article· en· W4397043219 on OpenAlexaff
Min Jun, James Wick, Brendon L. Neuen, Sradha Kotwal, Sunil V. Badve, John Chalmers, Meg Jardine, Vlado Perkovic, Martin Gallagher, Paul E. Ronksley

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrimary careMedicineGeneral practiceFamily medicinePediatricsGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Background: CKD prevalence in Australia varies substantially across reports. Using a large, nationally-representative general practice data source in Australia, we determined the contemporary prevalence and staging of CKD in Australian primary care. Methods: We performed a retrospective, community-based observational study using healthcare data from MedicineInsight, a national general practice data source in Australia. We included all adults with ≥1 visit to a general practice participating in MedicineInsight and ≥1 serum creatinine measurement (with or without a UACR measure) between 2011-2020; n=2,720,529 patients. CKD prevalence was estimated using 3 definitions: (1): an eGFR (mL/min/1.73m2) <60 or an eGFR ≥60 with a UACR (mg/mmol) >2.5 for M and >3.5 for F, (2) 2 consecutive eGFR measures <60, ≥90 days apart or an eGFR ≥60 with a UACR >2.5 for M and >3.5 for F and (3) 2 consecutive eGFR measures <60, ≥90 days part and/or 2 consecutive UACR measures >2.5 for M and >3.5 for F ≥90 days apart. Patient characteristics were assessed across the 3 definitions. Results: CKD prevalence progressively increased over the 10-year study period, irrespective of the method used to define CKD. The annual prevalence of CKD varied across the 3 CKD definitions, with definition 1 resulting in the highest estimates. In 2020, CKD prevalence in the study cohort was 8.4% (n=123,988), 4.7% (n=69,110) and 3.1% (n=45,360) using definitions 1, 2 and 3, respectively. The number of patients with UACR measurements was low such that, among those identified as having CKD in 2020, only 3.8%, 3.2% and 1.5% respectively, had both eGFR and UACR measures available in the corresponding year. Patients in whom both eGFR and UACR measurements were available mostly had moderate or high risk of CKD progression (83.6%, 80.6% and 76.2%, respectively). Comorbid burden in patients with CKD was also frequently observed. Conclusions: In this large, nationally-representative study, we observed an increasing trend in CKD prevalence in primary care settings in Australia. Most patients with CKD were at moderate to high risk of CKD progression with a significant comorbid burden. These findings highlight the need for early detection and effective management to slow progression of CKD. Funding: Commercial Support - This study was supported by an unrestricted research grant from Boehringer Ingelheim.

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)0.006
metaresearch head score (Gemma)0.018
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.464

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.353
GPT teacher head0.543
Teacher spread0.190 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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