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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".