Clinicopathological characteristics and outcome predictors of anti-glomerular basement membrane glomerulonephritis
Bibliographic record
Abstract
To explore the clinicopathological features of anti-glomerular basement membrane (anti-GBM) glomerulonephritis (anti-GBM-GN) and the prognostic values of clinical and laboratory indicators at diagnosis on renal and patient survival. A total of 76 patients (34 males and 42 females) with anti-GBM-GN who were hospitalized in the First Affiliated Hospital of Nanjing Medical University between January 2010 and June 2021 were included in this study. The baseline clinical features, histopathological data from renal biopsies, and predictors of renal and patient survival were retrospectively analyzed. Among the 76 patients, the median serum creatinine at diagnosis was 618.0 (350.98, 888.25) μmol/L and the median estimated glomerular filtration rate (eGFR) was 6.62 (4.39, 14.41) mL/min. Of these 76 patients, 55 (72.4%) received initial kidney replacement therapy (KRT) and 39 (51.3%) received plasma exchange or double-filtered plasmapheresis (DFPP). During a median follow-up duration of 28.5 (6.0, 71.8) months, 53 (69.7%) patients progressed to kidney failure with replacement therapy (KFRT) and received maintenance dialysis. Initial KRT (HR = 3.48, 95% CI = 1.22–9.97, p = 0.020) was a significant risk factor for renal survival. During the follow-up, 49 (64.5%) of 76 patients survived. Age (≥60 years, HR = 4.13, 95% CI = 1.65–10.38, p = 0.003) and initial KRT (HR = 2.87, 95% CI = 1.01–8.14, p = 0.047) were predictive of patient survival. Among patients with anti-GBM-GN, initial KRT at presentation was predictive of KFRT while older age and initial KRT were associated with higher all-cause mortality.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".