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Record W4412111092 · doi:10.1681/asn.0000000796

Predictive Value of the Oxford Classification for the Effect of Glucocorticoid Therapy in IgA Nephropathy

2025· article· en· W4412111092 on OpenAlexaff
Sufang Shi, Ian S.D. Roberts, Lei Jiang, Chen Tang, Jinwei Wang, Jicheng Lv, Muh Geot Wong, Sean J. Barbour, Vlado Perkovic, Daniel C. Cattran, Hong Zhang

Bibliographic record

VenueJournal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of British ColumbiaUniversity Health Network
FundersNational Natural Science Foundation of China
KeywordsPredictive valueGlucocorticoidNephropathyMedicineValue (mathematics)Internal medicineImmunologyEndocrinologyMathematicsStatistics

Abstract

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Key Points The presence of cellular segmental sclerosis and crescents predicts a favorable response to glucocorticoid therapy in patients with IgA nephropathy. Glucocorticoid therapy was associated with lower risk of kidney failure across histologic subtypes, with the greatest benefit in cellular segmental sclerosis. The study highlights that there is a need for further subclassification of segmental sclerosis lesions that may guide therapeutic decisions. Background The Oxford Classification is widely accepted as a histopathology tool to predict kidney outcomes in IgA nephropathy. However, it remains unclear whether the mesangial hypercellularity (M), endocapillary proliferation (E), segmental glomerulosclerosis (S), tubular atrophy/interstitial fibrosis (T), and crescents (C) scores can predict therapeutic response. This study aims to determine the predictive value of mesangial hypercellularity (M), endocapillary proliferation (E), segmental glomerulosclerosis (S), tubular atrophy/interstitial fibrosis (T), and crescents (C) scores on the efficacy of glucocorticoid therapy using the Therapeutic Effects of Steroids in IgA Nephropathy Global trial. Methods Three hundred and seventy-nine Chinese participants were enrolled in the Therapeutic Effects of Steroids in IgA Nephropathy Global trial, of whom 279 had kidney biopsy slides available for central pathology review. The primary outcomes were a composite of ≥40% reduction in eGFR, kidney failure, or death due to kidney disease. Multivariable Cox regression analysis was used to determine the effects of glucocorticoid therapy across pathologic subgroups, and the interaction between glucocorticoid therapy and pathology scores was evaluated. Results Among 279 participants selected for this study, the median (interquartile range) time from kidney biopsy to randomization was 4 (3–7) months. The median (interquartile range) follow-up was 4.7 (3.0–6.4) and 5.1 (3.1–6.8) years for the placebo and glucocorticoid-treated group. Glucocorticoid therapy showed benefits across all histologic subtypes. Participants with crescents (C1/C2) showed a trend toward greater benefit from glucocorticoid therapy (C1/2: hazard ratio [HR], 0.05 [95% confidence interval (CI), 0.008 to 0.3]; C0: HR, 0.6 [95% CI, 0.4 to 0.9]; P for interaction = 0.4). Participants with hypercellularity within segmental sclerosis lesions (cellular segmental sclerosis) demonstrated a significant reduction in the risk of kidney failure compared with those without (HR, 0.2 [95% CI, 0.07 to 0.4] versus HR, 0.6 [95% CI, 0.4 to 1.0]; P for interaction = 0.03). Analysis of local pathologists' scores of all 379 Chinese participants demonstrated a significantly greater benefit from glucocorticoid therapy in participants with crescents (C0: HR, 0.7 [95% CI, 0.4 to 1.2]; C1: HR, 0.3 [95% CI, 0.2 to 0.6]; C2: HR, 0.2 [95% CI, 0.08 to 0.7]; P for interaction = 0.05). Conclusions The presence of crescents and cellular segmental sclerosis in patients with IgA nephropathy was associated with a favorable response to glucocorticoid therapy.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.292
Teacher spread0.282 · 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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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Same venueJournal of the American Society of NephrologySame topicRenal Diseases and GlomerulopathiesFrench-language works237,207