Assessment of renal perfusion with 82-rubidium PET in patients with normal and abnormal renal function
Bibliographic record
Abstract
BACKGROUND: Noninvasive measurement of renal blood flow (RBF) and renal vascular resistance (RVR) is challenging, yet critical in renal pathologies. This study evaluates the correlation between serum renal function markers and RBF/RVR assessed using rubidium PET. METHODS: Dynamic images from 53 patients who underwent rubidium PET for nonrenal indications were analyzed. RBF was determined using a one-compartment model, and RVR was calculated by dividing mean arterial pressure by RBF. RESULTS: The study included 51 patients (31 females and 20 males). Among them, 35 had normal renal function [estimated glomerular filtration rate (eGFR) ≥60 ml/min/1.73 m 2 ], and 16 had abnormal renal function (eGFR <60 ml/min/1.73 m 2 ). Patients with normal renal function had significantly higher RBF [median (interquartile range): 443 (297-722) vs 173 (108-380) ml/min/100 g, P = 0.022] and lower RVR [19.1 (12.4-27.2) vs 49.6 (24.4-85.7) mmHg×min×g/ml, P = 0.0011) compared with those with abnormal renal function. There was a moderate correlation between RBF and eGFR ( r = 0.62, P < 0.0001) and between RVR and eGFR ( r = -0.59, P < 0.0001) in both groups. Among patients with normal renal function, RBF was negatively correlated with age ( r = -0.51, P = 0.0017) but there was no correlation among patients with abnormal renal function ( r = 0.21, P = 0.44). CONCLUSION: PET-measured RBF and RVR correlate with renal function markers and differ significantly by renal function status. Further studies are needed to validate rubidium PET's precision and clinical applicability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".