Use of administrative databases for health-care planning in CKD
Bibliographic record
Abstract
Good-quality information is required to plan healthcare services for patients with chronic diseases. Such information includes measures of disease burden, current care patterns and gaps in care based on quality-of-care indicators and clinical outcomes. Administrative data have long been used as a source of information for policy decisions related to the management of chronic diseases including cardiovascular disease, diabetes and hypertension. More recently, chronic kidney disease (CKD) has been acknowledged as a significant public health issue. Administrative data, particularly when supplemented by the use of routine laboratory data, have the potential to inform the development of optimal CKD care strategies, generate hypotheses about how to slow disease progression and identify risk factors for adverse outcomes. Available data may allow case identification and assessment of rates and patterns of disease progression, evaluation of risk and complications, including current gaps in care, and an estimation of associated costs. In this article, we use the example of the Alberta Kidney Disease Network to describe how researchers and policy makers can collaborate, using administrative data sources to guide health policy for the care of CKD patients.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".