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Record W3087918056 · doi:10.1002/jmri.27360

Hemorrhagic Cysts and Other <scp>MR</scp> Biomarkers for Predicting Renal Dysfunction Progression in Autosomal Dominant Polycystic Kidney Disease

2020· article· en· W3087918056 on OpenAlexaff
Sadjad Riyahi, Hreedi Dev, Jon D. Blumenfeld, Hanna Rennert, Xiaorui Yin, Hanieh Attari, Irina Barash, Ines Chicos, Warren O. Bobb, Stephanie Donahue, Martin R. Prince

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsColumbia College
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineAutosomal dominant polycystic kidney diseasePathologyDiseaseKidneyPolycystic kidney diseaseInternal medicine

Abstract

fetched live from OpenAlex

Background Screening for rapidly progressing autosomal dominant polycystic kidney disease (ADPKD) is necessary for assigning and monitoring therapies. Height‐adjusted total kidney volume (ht‐TKV) is an accepted biomarker for clinical prognostication, but represents only a small fraction of information on abdominal MRI. Purpose To investigate the utility of other MR features of ADPKD to predict progression. Study Type Single‐center retrospective. Population Longitudinal data from 186 ADPKD subjects with baseline serum creatinine, PKD gene testing, abdominal MRI measurements, and ≥2 follow‐up serum creatinine were reviewed. Field Strength/Sequence 1.5T, T 2 ‐weighted single‐shot fast spin echo, T 1 ‐weighted 3D spoiled gradient echo (liver accelerated volume acquisition) and 2D cine velocity encoded gradient echo (phase contrast MRA ). Assessment Ht‐TKV, renal blood flow (RBF), number and fraction of renal and hepatic cysts, bright T 1 hemorrhagic renal cysts, and liver and spleen volumes were independently assessed by three observers blinded to estimated glomerular filtration rate (eGFR) data. Statistical Tests Linear mixed‐effect models were applied to predict eGFR over time using MRI features at baseline adjusted for confounders. Validation was performed in 158 patients who had follow‐up MRI using receiver operator characteristic, sensitivity, and specificity. Results Hemorrhagic cysts, fraction of renal and hepatic cysts, height‐adjusted liver and spleen volumes were significant independent predictors of future eGFR (final prediction model R 2 = 0.88 P &lt; 0.05). The number of hemorrhagic cysts significantly improved the prediction compared to ht‐TKV in predicting future eGFR (area under the curve [AUC] = 0.94, 95% confidence interval [CI]: 0.9–0.94 vs. R 2 = 0.9, 95% CI: 0.85–0.9, P = 0.045). For baseline eGFR ≥60 ml/min/1.73m 2 , sensitivity for predicting eGFR&lt;45 ml/min/1.73m 2 by ht‐TKV alone was 29%. Sensitivity increased to 72% with all MRI variables in the model ( P &lt; 0.05 = 0.019), whereas specificity was unchanged, 100% vs. 99%. Data Conclusion Combining multiple MR features including hemorrhagic renal cysts, renal cyst fraction, liver and spleen volume, hepatic cyst fraction, and renal blood flow enhanced sensitivity for predicting eGFR decline in ADPKD compared to the standard model including only ht‐TKV. Level of Evidence 2 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:564–576.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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Citations23
Published2020
Admission routes1
Has abstractyes

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