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Record W2911072514 · doi:10.1002/jmri.26627

MRI in autosomal dominant polycystic kidney disease

2019· review· en· W2911072514 on OpenAlexaff
Weiguo Zhang, Jon D. Blumenfeld, Martin R. Prince

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsColumbia College
Fundersnot available
KeywordsAutosomal dominant polycystic kidney diseaseMedicineMagnetic resonance imagingBiomarkerRadiologyDiseaseRenal functionImaging biomarkerStage (stratigraphy)KidneyPathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Magnetic resonance imaging (MRI) is increasingly used in autosomal dominant polycystic kidney disease (ADPKD) for diagnosis, classification, assessment of disease progression and treatment response, and for identifying complications. Herein we review the role of MRI in the management of patients with ADPKD. We show how MRI-derived total kidney volume is a biomarker for assessing ADPKD severity and predicting decline in renal function. We also demonstrate the MR appearances of common complications. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2019;50:41-51.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.285
Teacher spread0.273 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations21
Published2019
Admission routes1
Has abstractyes

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