Dipstick Proteinuria as a Screening Strategy to Identify Rapid Renal Decline
Bibliographic record
Abstract
Rapid kidney function decline (RKFD) predicts cardiovascular morbidity and mortality, but serial assessment of estimated GFR (eGFR) is not cost-effective for the general population. Here, we evaluated the predictive value of albuminuria and three thresholds of dipstick proteinuria to identify RKFD in 2,574 participants in a community-based prospective cohort study with a median of 7 years follow-up. Median change in eGFR was -0.78 ml/min per 1.73 m(2) per year; with 8.5% experiencing RKFD, defined as a >5% annual eGFR decline from baseline. Of those with RKFD, 65% advanced to a new CKD stage compared with 19% of those without RKFD. Dipstick protein ≥ 1 g/L was a stronger predictor of RKFD than albuminuria. Overall, 2.5% screened positive for dipstick protein ≥ 1 g/L at baseline; one of every 2.6 patients would have RKFD if all were followed with serial eGFR measurement. Overall, the screening strategy correctly identified progression status for 90.8% of patients, mislabeled 1.5% as RKFD, and missed 7.7% with eventual RKFD. Among those with risk factors (cardiovascular disease, age >60, diabetes, or hypertension), the probability of identifying RKFD from serial eGFR measurements increased from 13 to 44% after incorporating dipstick protein (≥ 1 g/L threshold). In summary, inexpensive screening with urine dipstick should allow primary care physicians to follow fewer patients with serial eGFR assessment but still identify those with rapid decline of kidney function.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".