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Record W2891613862 · doi:10.1002/jmri.26289

Relationship of Seminal Megavesicles, Prostate Median Cysts, and Genotype in Autosomal Dominant Polycystic Kidney Disease

2018· article· en· W2891613862 on OpenAlexaff
Weiguo Zhang, Chelsea J. Stephens, Jon D. Blumenfeld, Ashkan Heshmatzadeh Behzadi, Stephanie Donahue, Warren O. Bobb, Jeffrey H. Newhouse, Hanna Rennert, Yize Zhao, Martin R. Prince

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2018
Typearticle
Languageen
FieldMedicine
TopicUrologic and reproductive health conditions
Canadian institutionsColumbia College
FundersNational Center for Advancing Translational SciencesNational Institutes of Health
KeywordsPKD1Autosomal dominant polycystic kidney diseaseProstateMedicineCystSeminal vesicleUrologyPolycystic kidney diseaseKidney diseaseEjaculatory ductInternal medicineKidneyProstate cancerPathologyGastroenterologyEndocrinologyCancer

Abstract

fetched live from OpenAlex

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) can involve prostate and seminal vesicles but the potential interrelationship of these findings and associations with PKD gene mutation locus and type is unknown. PURPOSE: To determine the interrelationship of seminal megavesicles (seminal vesicles with lumen diameter > 10mm) and prostatic cysts in ADPKD and to determine whether there are associations with PKD gene mutations. STUDY TYPE: Retrospective, case control. POPULATION: Male ADPKD subjects (n = 92) with mutations in PKD1 (n = 71; 77%) or PKD2 (n = 21; 23%), and age/gender-matched controls without ADPKD (n = 92). FIELD STRENGTH/SEQUENCE: -weighted MR images. ASSESSMENT: Reviewers blinded to genotype independently measured seminal vesicle lumen diameter and prevalence of cysts in prostate, kidney, and liver. STATISTICAL TESTS: Nonparametric tests for group comparisons and univariate and multivariable logistic regression analyses to identify associations of megavesicles and prostate median cysts with mutations and renal/hepatic cyst burden. RESULTS: Seminal megavesicles were found in 23 of 92 ADPKD (25%) subjects with PKD1 (22/71, 31%) or PKD2 (n = 1/21, 5%) mutations, but in only two control subjects (P < 0.0001). Prostate median cysts were found in 17/92 (18%) ADPKD subjects, compared with only 6/92 (7%) controls (P = 0.01), and were correlated with seminal vesicle diameters (ρ = 0.24, P = 0.02). Nonmedian prostate cyst prevalence was identical between ADPKD and controls (7/92, 8%). After adjusting for age, estimated glomerular filtration rate, and height-adjusted total kidney volume, ADPKD subjects with megavesicles were 10 times more likely to have a PKD1 than a PKD2 mutation. Among PKD1 subjects, seminal megavesicles occurred more frequently with nontruncating mutations with less severe kidney involvement. DATA CONCLUSION: ADPKD is associated with prostate median cysts near ejaculatory ducts. These cysts correlate with seminal megavesicles (dilated to >10 mm) which predict a 10-fold greater likelihood of PKD1 vs. PKD2 mutation. Cysts elsewhere in the prostate are not related to ADPKD. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:894-903.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.278
Teacher spread0.266 · 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 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".

Quick stats

Citations15
Published2018
Admission routes1
Has abstractyes

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