Derivation and validation of a clinical index for prediction of rapid progression of kidney dysfunction
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease is common among the elderly, and these patients are at risk of progressive kidney dysfunction. AIM: To develop an index to predict rapid progression of kidney dysfunction. DESIGN: Community-based cohort divided into derivation (n = 6789) and validation (n = 3395) subsets. METHODS: We identified 10 184 subjects aged >/=66 years from computerized laboratory data. Prescription drug data was used to define disease categories and medication exposure, and an index for predicting rapid progression of kidney dysfunction (> or =25% decline in glomerular filtration rate over a 2-year period) was obtained from a logistic regression model in the derivation cohort. The risk score for each subject was calculated by summing the component variables together, which were subsequently categorized into five risk classes. RESULTS: Five predictors of rapid progression were identified: age >75 years, cardiac disease, diabetes mellitus, gout, and use of anti-emetic medications. Rates of rapid progression for risk classes I through V were 8.6%, 10.9%, 13.9%, 15.6%, and 24.1%, respectively, for the derivation cohort, and 8.4%, 11.6%, 15.5%, 17.3%, 21.9%, respectively, for the validation cohort. The risk index distinguished between low and high risk of rapid progression, with a 2.5-fold greater risk for the highest, compared to the lowest, risk decile. DISCUSSION: Readily available clinical data can be used to identify most elderly at risk of rapid progression of kidney dysfunction. This simple index could help clinicians to identify patients at risk, and implement strategies to slow the progression of kidney dysfunction.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".