MO645: Does Sodium Magnetic Resonance Imaging Help for Initiation of Incremental Dialysis?
Bibliographic record
Abstract
Abstract BACKGROUND AND AIMS Incremental hemodialysis (HD) (twice a week) can be proposed to some patients who had a significant residual renal function (RRF) when HD is initiated. However, renal urea clearance is a very limited tool to predict the ability of the kidney to extract significantly water and sodium. We hypothesized that corticomedullary gradient (CMG) measurement with 23NaMRI could provide a new tool to select HD patients for incremental dialysis. We have already validated the use of 23NaMRI in healthy control. However, we had to prove first the feasibility of such a measure for HD patients. METHOD We conducted a prospective observational study to better characterize CMG in HD patients with 23NaMRI. We performed blood (urea, creatinine, sodium), urine collection (osmolarity, creatinine, sodium) and CMG with 23NaMRI in fasting patients. All MR experiments were carried out on a GE MR750 3T (GE Healthcare, WI). A custom-built two-loop (18 cm 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 = 4) with or without RRF. RESULTS For healthy controls, median (IQR) age was 50 (32–60) years, 46% men, estimated glomerular filtration rate (eGFR) 103 (84–108) mL/min/1.73m2 urinary osmolarity (osmU) 786 (587–938) osm/L, sodium excretion fraction (FeNa) 0.27% (0.21–0.49). For HD patients, median (IQR) age was 68 (60–73) years, 50% men, creatinine 499 (366–606) umol/L, urinary osmolarity (osmU) 279 (193–317) osm/L, sodium excretion fraction (FeNa) 1.14% (0.44–2.21). Corticomedullary gradient for controls [1.53 (1.47–1.61)], was significantly different to HD 1.28 (1.17–1.4) (P = .001) as expected. There was a significant correlation between osmolarity and CMG (r = 0.73, P < .001). We showed a significant association between CMG and age (P = .03). The patient with RRF (diuresis to 1.3L/day) didn't display a different CMG compared with patients without RRF. Figure 1 shows the difference in corticomedullary pictures (A) control and (B) HD patients. CONCLUSION We showed that it is possible to assess corticomedullary gradient in HD patients. More patients are needed to explore the ability of the 23NaMRI to discriminate patients who could benefit from incremental dialysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".