Reproducibility of the Oxford classification of immunoglobulin A nephropathy, impact of biopsy scoring on treatment allocation and clinical relevance of disagreements: evidence from the VALidation of IGA study cohort
Bibliographic record
Abstract
BACKGROUND: The VALidation of IGA (VALIGA) study investigated the utility of the Oxford Classification of immunoglobulin A nephropathy (IgAN) in 1147 patients from 13 European countries. Methods. Biopsies were scored by local pathologists followed by central review in Oxford. We had two distinct objectives: to assess how closely pathology findings were associated with the decision to give corticosteroid/immunosuppressive (CS/IS) treatments, and to determine the impact of differences in MEST-C scoring between central and local pathologists on the clinical value of the Oxford Classification. We tested for each lesion the associations between the type of agreement (local and central pathologists scoring absent, local present and central absent, local absent and central present, both scoring present) with the initial clinical assessment, as well as long-term outcomes in those patients who did not receive CS/IS. RESULTS: All glomerular lesions (M, E, C and S) assessed by local pathologists were independently associated with the decision to administer CS/IS therapy, while the severity of tubulointerstitial lesions was not. Reproducibility between local and central pathologists was moderate for S (segmental sclerosis) and T (tubular atrophy/interstitial fibrosis), and poor for M (mesangial hypercellularity), E (endocapillary hypercellularity) and C (crescents). Local pathologists found statistically more of each lesion, except for the S lesion, which was more frequent with central review. Disagreements were more likely to occur when the proportion of glomeruli affected was low. The M lesion, assessed by central pathologists, correlated better with the severity of the disease at presentation and discriminated better with outcomes. In contrast, the E lesion, evaluated by local pathologists, correlated better with the clinical presentation and outcomes when compared with central review. Both C and S lesions, when discordant between local and central pathologists, had a clinical phenotype intermediate to double absent lesions (milder disease) and double present (more severe). CONCLUSION: We conclude that differences in the scoring of MEST-C criteria between local pathologists and a central reviewer have a significant impact on the prognostic value of the Oxford Classification. Since the decision to offer immunosuppressive therapy in this cohort was intimately associated with the MEST-C score, this study indicates a need for a more detailed guidance for pathologists in the scoring of IgAN biopsies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".