The Renal Resistive Index: A New Biomarker for the Follow-up of Vascular Modifications in Systemic Sclerosis
Bibliographic record
Abstract
Objective. The aim of the present retrospective observational study was to evaluate the change of Renal Resistive Index (RRI) over time (ΔRRI) and under treatment in patients with systemic sclerosis (SSc) as well as to correlate these changes with disease complications. Methods. Two hundred thirty patients [29 male, median age 57 (IQR 48–67) yrs] were enrolled. At baseline and follow-up (3.43, IQR 2.81–4.45 yrs), we collected the following data: disease variables, nailfold videocapillaroscopy (NVC) pattern, forced vital capacity (FVC), diffusing lung capacity for carbon monoxide (DLCO), systolic pulmonary arterial pressure (sPAP), presence of interstitial lung disease, RRI, evaluation of glomerular filtration rate, and new onset of pulmonary arterial hypertension (PAH). Results. RRI value is high in SSc patients with digital ulcers and anticentromere antibodies, active and late NVC patterns, and limited cutaneous SSc. A significant correlation was observed between ΔRRI and ΔsPAP (R = 0.17, P = 0.02), with statistically higher ΔRRI (0.08 ± 0.02 vs 0.03 ± 0.05, P = 0.04) in patients complicated by PAH onset. No other new-onset complication was associated with ΔRRI. The receiver-operating characteristic curve analysis confirmed the predictive role of ΔRRI in development of new PAH (area under the curve 0.84, 95% CI 0.75–0.93, P = 0.02). In patients with SSc never exposed to sildenafil, ΔRRI was higher (0.04 ± 0.05) compared to both patients exposed to sildenafil during the study period (0.01 ± 0.05, P = 0.03) or in those exposed at the time of baseline evaluation (0.00 ± 0.05, P = 0.01). Conclusion. RRI and its variation in time are a reliable marker of SSc-related vasculopathy, both in renal and extrarenal compartments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".