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Record W3007006547 · doi:10.1681/asn.2019080825

Ultrastructural Characterization of Proteinuric Patients Predicts Clinical Outcomes

2020· article· en· W3007006547 on OpenAlexaff
Virginie Royal, Jarcy Zee, Qian Liu, Carmen Ávila-Casado, Abigail R. Smith, Gang Liu, Laura H. Mariani, Stephen M. Hewitt, Lawrence B. Holzman, Brenda W. Gillespie, Jeffrey B. Hodgin, Laura Barisoni

Bibliographic record

VenueJournal of the American Society of Nephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsThe Scarborough HospitalUniversity of TorontoUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersHalpin FoundationNational Center for Advancing Translational SciencesUniversity of MichiganNational Institute of Diabetes and Digestive and Kidney DiseasesNephcure Foundation
KeywordsUltrastructureMedicineKidney diseaseInternal medicinePathologyUrology

Abstract

fetched live from OpenAlex

Significance Statement Glomerular features ascertained by electron microscopy are underreported in clinical practice, and their value in predicting outcome is unclear. This study is the first comprehensive investigation of the association of clinical outcomes with 12 glomerular electron microscopy descriptors reflecting the status of podocytes, endothelial cells, and glomerular basement membranes, individually and as electron microscopy profiles after descriptor-based consensus clustering. The authors demonstrate that severe effacement and microvillous transformation, individually and as a component of clusters, were associated with proteinuria remission, whereas prominent endothelial cell and glomerular basement membrane abnormalities were associated with loss of renal function. These findings highlight the importance of a standardized and comprehensive ultrastructural analysis, and that use of quantifiable structural changes in assessing patients with proteinuria might have important clinical implications. Background The analysis and reporting of glomerular features ascertained by electron microscopy are limited to few parameters with minimal predictive value, despite some contributions to disease diagnoses. Methods We investigated the prognostic value of 12 electron microscopy histologic and ultrastructural changes (descriptors) from the Nephrotic Syndrome Study Network (NEPTUNE) Digital Pathology Scoring System. Study pathologists scored 12 descriptors in NEPTUNE renal biopsies from 242 patients with minimal change disease or FSGS, with duplicate readings to evaluate reproducibility. We performed consensus clustering of patients to identify unique electron microscopy profiles. For both individual descriptors and clusters, we used Cox regression models to assess associations with time from biopsy to proteinuria remission and time to a composite progression outcome (≥40% decline in eGFR, with eGFR<60 ml/min per 1.73 m 2 , or ESKD), and linear mixed models for longitudinal eGFR measures. Results Intrarater and interrater reproducibility was >0.60 for 12 out of 12 and seven out of 12 descriptors, respectively. Individual podocyte descriptors such as effacement and microvillous transformation were associated with complete remission, whereas endothelial cell and glomerular basement membrane abnormalities were associated with progression. We identified six descriptor-based clusters with distinct electron microscopy profiles and clinical outcomes. Patients in a cluster with more prominent foot process effacement and microvillous transformation had the highest rates of complete proteinuria remission, whereas patients in clusters with extensive loss of primary processes and endothelial cell damage had the highest rates of the composite progression outcome. Conclusions Systematic analysis of electron microscopic findings reveals clusters of findings associated with either proteinuria remission or disease progression.

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)0.003
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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

Citations46
Published2020
Admission routes1
Has abstractyes

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