Hypertension-based clinical risk strategies for detecting microalbuminuria in diabetes
Bibliographic record
Abstract
BACKGROUND: Microalbuminuria screening to identify patients at risk of diabetic nephropathy is widely accepted. AIM: To investigate whether blood-pressure-based strategies can identify such patients without the need for microalbuminuria testing. METHODS: Spot urine for albumin/creatinine ratios was performed in all patients over an 18-month period. The performance of four combinations of clinical models, based on existing triggers for anti-hypertensive intervention (prior use and/or existing systolic BP exceeding 140 or 160 mmHg and/or dipstick proteinuria exceeding 1+ or 2+) was evaluated at microalbuminuria thresholds of 3.5 and 10 mg/mmol. The models were ranked 1 to 4, based on their escalating relative strengths in predicting need for intervention. RESULTS: Of 3748 patients, 1257 (34%) or 739 (20%) exceeded microalbuminuria thresholds of 3.5 or 10 mg/mmol. All four models predicted microalbuminuria risk (areas under ROC curves 0.60-0.77, all p < 0.001). The models (1-4) identified 2220, 2465, 2803 or 2937 for intervention, respectively, irrespective of microalbuminuria status, and missed 368, 232, 194 or 126 at 3.5 mg/mmol and 164, 87, 81 or 45 at 10 mg/mmol. DISCUSSION: Clinical models using routinely measured parameters reduced the target population for microalbuminuria screening by 60-80%, missing 3-10% of patients with albumin/creatinine ratios exceeding 3.5 mg/mmol or 1-4% of those exceeding 10 mg/mmol.
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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 | 0.021 |
| 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 | 0.001 |
| 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".