Renal Parenchymal Thickness in Patients with Systemic Sclerosis Is Related to Intrarenal Hemodynamic Variables and Raynaud Renal Phenomenon
Bibliographic record
Abstract
OBJECTIVE: Renal involvement in systemic sclerosis (SSc) ranges from urinary abnormalities, reduction of glomerular filtration rate, and high renal resistive index, to scleroderma renal crisis. Intrarenal resistance indices are considered markers of renal SSc-associated vasculopathy. The aim of this study is to evaluate renal morphological variables, such as renal length, parenchymal thickness, atrophy index, and renal sinus in patients with SSc and to correlate it with renal function and hemodynamic variables. METHODS: There were 92 patients with SSc and 40 healthy controls (HC) enrolled in this study. Doppler and renal ultrasound (US) including renal length, parenchymal thickness, atrophy index, renal sinus, and intrarenal resistive index were measured in patients with SSc and HC. RESULTS: Renal US showed significant differences between HC and patients with SSc. The renal length (mm; 106.7 ± 5.1 vs 102.3 ± 8.4) and renal sinus (70.7 ± 7.9 vs 65.3 ± 7.7 mm) were significantly (p = 0.001) higher in HC than patients with SSc. The parenchymal thickness was significantly (p = 0.004) higher in HC than patients with SSc (18 ± 3.1 vs 16.3 ± 2.5 mm). Pulsatility index, resistive index, and systolic/diastolic ratio were significantly (p < 0.0001) lower in HC than patients with SSc. The renal length was significantly (p = 0.004) higher in diffuse cutaneous SSc (105 ± 8.4) than in limited cutaneous SSc (99.5 ± 7.5). CONCLUSION: In SSc, kidney involvement is subclinical and is related to vascular injury, Raynaud phenomenon, and chronic hypoxia that can modify renal morphology. Serum creatinine is a poor marker of renal damage, and renal US could be a useful tool - together with Doppler - to evaluate renal involvement in a systemic and chronic disease such as SSc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".