Does Sodium Magnetic Resonance Imaging Help for Initiation of Incremental Dialysis?
Bibliographic record
Abstract
Background: Incremental HD (twice a week) can be proposed to some patients who had a significant residual renal function (RRF) when hemodialysis (HD) is initiated. However renal urea clearance is a very limited tool to assess the totality of crucial functions of the kidney, such as ability of the kidney to control salt and water excretion. We hypothesized that corticomedullary gradient (CMG) measurement with 23NaMRI could provide a new tool to select HD patients potentially suitable for incremental dialysis. Methods: We conducted a prospective observational study to better characterize CMG in HD patient with 23NaMRI. We performed CMG measurment with 23NaMRI in fasting patients. All MR experiments were carried out on a GE MR750 3T (GE Healthcare, WI). A custom-built two-loop (18cm in diameter) butterfly radiofrequency surface coil tuned for 23Na frequency (33.786 MHz) was used to acquire renal 23Na images. We compared CMG in healthy controls (n=15) and HD patients (n=10) with or without conventionally assessed RRF. Results: For healthy controls, median (IQR) age was 50 (32-60), years old, 46% men, eGFR 103 (84-108) mL/min/1.73m2, urinary osmolarity (osmU) 786 (587-938) osm/L. For HD patients, median(IQR) age was 50 (32-60) years old, 40 % men, urinary osmolarity (osmU) 313 (193-317) osm/L, 40% with residual renal function (RRF). Corticomedullary gradient for controls (1,53 (1,47-1,61)), was significantly different to HD 1,32 (1,24-1,36) (p=0.001). There was a significant correlation between osmolarity and CMG (r=0.92, p<0.001). We were able to see a difference in salt repartition between HD patient with RRF and control. Anuric HD patients had lost medullary sodium entirely. Figure 1 shows difference in corticomedullary pictures (A) control; (B) HD patients with RRF; (C) HD patients with no RRF Conclusions: We showed that is possible to assess corticomedullary gradient in HD patients. Additional study is justified to explore the ability to the 23NaMRI to discriminate patients who might benefit best from an incremental dialysis approach.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".