Association of quantitative renal surface nodularity with the renal dysfunction progression in patients with arterial hypertension
Bibliographic record
Abstract
BACKGROUND: Hypertensive nephropathy, a major cause of end-stage renal disease, lacks reliable noninvasive biomarkers. Renal surface nodularity (RSN) on CT may reflect nephrosclerosis, but existing methods fail to quantify nodularity heterogeneity or predict renal decline. This study developed a novel CT-based RSN metric and assessed its prognostic value in hypertensive patients. METHODS: This retrospective cohort study included hypertensive patients who underwent contrast-enhanced CT. Patients with bilateral renal surface irregularities were assigned to the RSN group, with age- (± 2 year) and sex-matched controls (non-RSN group) randomly selected at a 1:1 ratio. RSN was quantified using three surface roughness metrics. A semi-quantitative RSN score was also calculated based on the distribution and depth (> 50% cortical thickness) of localized surface defects. The primary endpoint was composite renal dysfunction progression (≥ 25% eGFR decline or initiation of renal replacement therapy). The secondary endpoint was defined as an annual decline in eGFR > 5 ml/min/1.73 m² per year. RESULTS: A total of 242 patients were included (median age, 64 years; 70.25% male). Strong linear correlations were observed between quantitative RSN metrics and semi-quantitative score (all ρ > 0.9, P < 0.001). Over a median follow-up of 38.00 months (Interquartile range: 22.00-56.25), patients in the RSN group had a significantly higher risk of renal dysfunction progression (33 vs. 5 events; HR = 5.22, P < 0.001). After adjusting sex, age, hypertension grade, diabetes, hyperlipidemia, eGFR, and renal volume, renal dysfunction progression was independently associated with arithmetic mean deviation (HR = 1.798, 95% CI: 1.296-2.495, P < 0.001), maximum height (HR = 1.606, 95% CI: 1.177-2.191, P = 0.003), and ten-point unevenness height (HR = 2.239, 95%CI: 1.567-3.197, P < 0.001), respectively. The similar statistical results trend was present between RSN and the secondary endpoint. CONCLUSIONS: CT-based quantitative RSN was developed as a novel imaging biomarker for renal dysfunction progression in patients with arterial hypertension.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".