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Record W4210945196 · doi:10.1148/radiol.211238

Functional Sodium MRI Helps to Measure Corticomedullary Sodium Content in Normal and Diseased Human Kidneys

2022· article· en· W4210945196 on OpenAlexafffund
Alireza Akbari, Sandrine Lemoine, Fabio R. Salerno, Taylor Marcus, Tristan Duffy, Timothy J. Scholl, Guido Filler, Andrew A. House, Christopher W. McIntyre

Bibliographic record

VenueRadiology · 2022
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsLawson Health Research InstituteLondon Health Sciences CentreWestern University
FundersCanadian Institutes of Health Research
KeywordsMedicineIntraclass correlationOsmoleUrinary systemUrine osmolalityUrologyKidney diseaseOsmotic concentrationSodiumKidneyMagnetic resonance imagingInternal medicinePathologyRadiology

Abstract

fetched live from OpenAlex

Background To the knowledge of the authors, urinary osmolarity is the only tool currently available to assess kidney corticomedullary gradient (CMG). Comparisons between CMG and urinary osmolarity and the use of modalities such as sodium MRI to evaluate renal disease in humans are lacking. Purpose To investigate the ability of sodium MRI to measure CMG dynamics compared with urinary osmolarity after water load in healthy volunteers and CMG in participants with kidney disease. Materials and Methods A prospective study was conducted from July 2020 to January 2021 in fasting healthy volunteers undergoing water load and participants with chronic kidney disease (CKD) from cardiorenal syndrome included in a clinical trial. In both groups, CMG was estimated by measuring the medulla-to-cortex signal ratio from sodium MRI at 3.0 T. A custom-built two-loop (diameter, 18 cm) butterfly radiofrequency surface coil, tuned for sodium frequency (33.786 MHz), was used to acquire renal sodium images. Two independent observers measured all sodium MRI cortical and medullary values for each region of interest to compute the intraclass correlation coefficient. Pearson correlation was performed between urinary osmolarity and CMG. Results Five participants with CKD (mean age, 77 years ± 12 [standard deviation]; all men) and 10 healthy volunteers (mean age, 42 years ± 15; six men, four women) were evaluated. A reduction was observed between baseline and peak urinary dilution time for both mean medulla-to-cortex ratios (1.55 ± 0.11 to 1.31 ± 0.09, respectively; P < .001) and mean urinary osmolarity (756 mOsm/L ± 157 to 73 mOsm/L ± 14, respectively; P < .001) in healthy volunteers. Medulla-to-cortex and corresponding urinary osmolarity were correlated in both groups (r2 = 0.22; P < .001). Kidney sodium tissue content was successfully acquired in all five participants with CKD. The intraclass correlation coefficient measurement was 0.99 (P < .001). Conclusion Functional sodium MRI accurately depicted corticomedullary gradient (CMG) dynamic changes in healthy volunteers and demonstrated feasibility of CMG measurement in participants with reduced kidney function. Clinical trial registration no. NCT04170855. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Laustsen and Bøgh in this issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0130.016
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.046
GPT teacher head0.269
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations22
Published2022
Admission routes2
Has abstractyes

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