Exploration of chronic kidney disease screening, diagnosis and management in Australian general practice using electronic medical record data
Bibliographic record
Abstract
BACKGROUND: CKD is a common but under-recognised condition that places significant burden on the individual and the health system globally. Our study applied a set of primary care quality indicators originally developed and validated using Canadian primary care data for screening, diagnosis and monitoring of CKD. These indicators were then applied to a large primary care dataset to assess CKD detection and management practices in Australia. METHODS: We used de-identified data from the Patron repository, which contains data extracted from electronic medical records (EMRs) in Australian general practices. The 16 CKD indicators developed using Canadian EMR data were applied to this dataset. These indicators measured and reported on the use of clinical and pathological tests to diagnose and monitor CKD, the prescribing of antihypertensive and statin medications, and on rates of influenza immunisation. RESULTS: , 54.2% (14,254) underwent a repeat eGFR within six months and 28.8% (7,586) completed an ACR test. Of the patients recommended for screening based on the presence of risk factors, 76.1% had an eGFR performed within the last 18 months, whilst 34.2% had an ACR performed in the same period. Rates of monitoring of patients with CKD were slightly higher. A blood pressure had been recorded within the last 9 months in 71.1% of patients with CKD, and in 75.6% of the subset of patients with both diabetes and albuminuria. Around 45% of all patients with CKD were meeting their blood pressure targets at their last recording. CONCLUSIONS: The results of this study demonstrate that it is feasible to derive meaningful and informative indicators of CKD diagnosis and management from primary care EMR data in Australia, which are comparable with international data. The low rates of CKD documentation and pathology monitoring provide opportunities for quality improvement initiatives to reduce disease burden. CLINICAL TRIAL NUMBER: Not applicable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".