Future of kidney imaging: Functional magnetic resonance imaging and kidney disease progression
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease (CKD) which is a common cause of death has an increasing trend, but there is no established approach for predicting CKD progression yet. Functional magnetic resonance imaging (fMRI) studies such as blood oxygenation level-dependent MRI (BOLD-MRI), diffusion-weighted MRI (DWI-MRI), diffusion-tensor MRI (DTI-MRI) and arterial spin labelling MRI (ASL-MRI) are rising methods for the assessment of kidney functions in native and transplanted kidneys as well as the estimation of CKD progression. METHODS: Systematic literature review was performed through the Embase (Elsevier), Cochrane Central Register of Controlled Trials (Wiley), PubMed/Medline and Web of Science databases, and studies investigating the role of fMRI methods assessing kidney functions in native and transplanted kidneys, as well as the value of fMRI methods to predict CKD progression, were included. Working mechanisms, advantages and limitations of the fMRI modalities were reviewed, and three studies investigating the role of fMRI studies in kidney functions were analysed. RESULTS AND CONCLUSION: BOLD-MRI signal was found to be inversely correlated with annual eGFR change, and DWI/ADC (apparent diffusion coefficient map) values were shown to be correlated with annual eGFR decline. fMRI methods which are currently used for other systems can be utilized to provide more detailed information about kidney functions, and doctors should be ready to interpret kidney MRIs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".