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Record W4403832324 · doi:10.1681/asn.2024vexpvzee

Clinical and Histological Predictors of Outcomes in C3 Glomerulopathy

2024· article· en· W4403832324 on OpenAlexaff
Malak Ghaddar, Hannah J. Lomax-Browne, H. Terence Cook, Erica Daina, Marina Noris, Giuseppe Remuzzi, Manuel Praga, Fernando Caravaca‐Fontán, Dilshani Induruwage, Matthew C. Pickering, Sean Barbour

Bibliographic record

VenueJournal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineGlomerulopathyInternal medicineKidney GlomerulusUrologyPathologyGlomerulonephritisKidney

Abstract

fetched live from OpenAlex

Background: C3 glomerulopathy (C3G) is a rare disease that has significant overlap with idiopathic membranoproliferative glomerulonephritis (MPGN). Risk factors for kidney outcomes remains poorly defined, limited by small cohorts. We aimed todetermine risk factors for kidney outcomes and to incorporate them into a prediction model Methods: Using a cohort of 225 patients with C3G or idiopathic MPGN from three international centers, we evaluated the association between clinical and histologic variables at biopsy and the composite outcome of 30% decline in eGFR or ESKD using Cox proportional hazards models. A prediction model was derived and internally validated through bootstrap resampling Results: In a multivariable model, lower eGFR, paraprotein presence, and interstitial fibrosis were associated with higher risk of outcome, while native (versus transplant) disease and lower C4 levels were associated with lower risk (Table1). The prediction model including these variables performed well (R2D: 53.14%, C-statistic: 0.84 (95% CI 0.82-0.86), integrated calibration index: 0.31), maintaining robustness after internal validation. Adding proteinuria over time showed that a 50% reduction from baseline to <1g/day was associated with a lower risk of outcome (HR 0.35, 95% CI 0.12-0.97) Conclusion: In the largest C3G/MPGN study to date, baseline eGFR, paraprotein, interstitial fibrosis, low C4 and transplant status were independently associated with kidney outcome. These factors can be used in a prediction model to predict individual patient risk. A 50% reduction in proteinuria to <1g/day was associated with lower kidney risk, suggesting it may be an evidence-based definition of proteinuria remission to use in clinical trials Covariates for the risk of progression to the composite outcome in a multivariable model - Parameter Adjusted HR (95% CI) P value eGFR (per 1 log base 2-unit decrease) 2.11 (1.66-2.67) <0.001 Native kidney (versus transplant) 0.35 (0.20-0.60) <0.001 Paraprotein 4.42 (2.00-9.77) <0.001 Low C4 0.51 (0.29-0.90) 0.02 Interstitial fibrosis 1 2.03 (1.03-4.01) 0.04 2 3.01 (1.56-5.83) 0.001 3 4.68 (2.05-10.68) <0.001 Interstitial fibrosis score: 0 (<10%), 1 (10%-25%), 2 (26%-50%) and 3 (>50%)

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.043
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.023
GPT teacher head0.317
Teacher spread0.294 · 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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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