A simple risk score for chronic kidney disease using administrative data: A population-based cohort study
Bibliographic record
Abstract
Abstract Background We did this study to develop and validate a risk score for new chronic kidney disease (CKD), focusing on predictors that are typically available in Canadian administrative health datasets. Methods This was a retrospective population-based cohort study using data from the Alberta Kidney Disease Network database: 3,558,192 adult participants were followed from April 1, 2007 to March 31, 2019. We developed a simple score to predict reduced glomerular filtration rate using bootstrapping (100 iterations with replacement) and internally validated the score using the original dataset. Findings The final score had a maximum total of 9 points: age 50-70 years, moderate albuminuria, hypertension, diabetes and heart failure all received a single point, and age >70 years and severe albuminuria received three points. The C-statistic of the score for incident CKD was 0.9272 and the Brier score was 0.0053, indicating excellent discrimination. Graphical analysis demonstrated that predicted risk closely aligned with the observed risk of developing CKD, indicating a well-calibrated model. Interpretation We have derived and internally validated a risk score for new CKD which is suitable for application to routinely collected Canadian administrative health data. Funding David Freeze chair in health services research
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".