Intravoxel incoherent motion to assess brain microstructure and perfusion in patients with end‐stage renal disease
Bibliographic record
Abstract
Abstract Background and Purpose This study aimed to investigate the clinical value of intravoxel incoherent motion (IVIM) diffusion‐weighted imaging in evaluating the brain microstructure and perfusion changes in end‐stage renal disease (ESRD) patients. Methods The routine head MRI sequences and IVIM were performed on 40 ESRD patients and 30 healthy subjects. The IVIM was executed with 10 b ‐values varying from 0 to 1000 seconds/mm 2 . All subjects were evaluated on neuropsychological test. Laboratory tests were conducted for ESRD patients. Results Compared with the control group, increased slow apparent diffusion coefficient values (ADC slow ) were found in the left frontal lobe, hippocampus, bilateral temporal lobe, and the right occipital lobe ( p < .05), and increased fast ADC values (ADC fast ) were found in all regions of interest (all p < .001) in ESRD patients. In ESRD patients, ADC fast in right frontal lobe ( p = .041) and insular lobe ( p = .045) was negatively correlated with the Montreal Cognitive Assessment score (MoCA), and ADC fast in the right parietal lobe ( p = .009) and hippocampus ( p = .041) had positive correlation with hemoglobin levels. Using receiver operating characteristics (ROC) analysis, ADC fast in the right frontal lobe, insular lobe, hippocampus, and parietal lobe separately showed fair to good efficacy in differentiating ESRD patients from healthy subjects, with the area under the ROC ranging from .853 to .903. Conclusions The microstructure and perfusion of the brain were impaired in ESRD patients. ADC fast of the right frontal lobe, insular lobe, hippocampus, and parietal lobe could be effective biomarker for evaluating cognitive impairment in ESRD patients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".