Future of kidney imaging: Functional magnetic resonance imaging and kidney disease progression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it