Clinical Usefulness of a Prognostic Score in Histological Analysis of Renal Biopsy in Patients with Lupus Nephritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate active and chronic lesions in association with renal outcome according to the International Society of Nephrology/Renal Pathology Society classification in patients with lupus nephritis. METHODS: A retrospective analysis of 99 biopsy-proven subjects with lupus nephritis from 1990 to 2006 was performed in our center using the new classification. Each histological lesion was evaluated by multivariate survival analysis as predictive factor for renal insufficiency in patients with lupus nephritis, and independent predictors were graded to develop the prognostic score based on the regression coefficient. A receiver operating-characteristic curve based on the prognostic score was plotted to determine the most appropriate cutoff point. RESULTS: In class IV, the IV-G group tended to exhibit a worse renal outcome compared with the IV-S group, but the difference was not significant (log-rank test, p = 0.4330). Independent histological predictors of poor renal outcome were extracapillary proliferation, glomerular sclerosis, and fibrous crescents analyzed by Cox proportional hazards model, while predictors of favorable renal outcome were hyaline thrombi and fibrous adhesions. By the prognostic score, renal outcome was significantly worse in the group with the higher score (> or = 0.25) than in the group with the lower score (< 0.25) in class IV patients (log-rank test, p < 0.001). CONCLUSION: These results demonstrate the advantage of our prognostic score compared to subclasses in predicting the renal outcome of class IV patients [University Hospital Medical Information Network (UMIN) clinical trials registry, number UMIN 000001943].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".