Predictors of Treatment Resistance and Relapse in Chinese Patients with Antineutrophil Cytoplasmic Antibody-associated Disease
Bibliographic record
Abstract
OBJECTIVE: The prevalence and significance of treatment resistance and relapse in patients from China with antineutrophil cytoplasmic antibody-associated (ANCA) disease are poorly understood. METHODS: A total of 98 patients with ANCA vasculitis, diagnosed between January 2003 and December 2009 in the China-Japan Friendship Hospital, were enrolled in this retrospective study. RESULTS: Fifteen patients (15.3%) were categorized as having cytoplasmic and/or proteinase 3 (PR3) ANCA and 83 patients (84.7%) had perinuclear and/or myeloperoxidase (MPO) ANCA. After the induction phase treatment, the disease was resistant to therapy in 24 (25%) of the patients. A response to initial treatment occurred in 74 patients (75%). Of these 74 patients, remission was achieved and sustained with or without maintenance therapy in 41 patients (55%). Multivariable logistic regression models revealed that female sex was a statistically significant predictor of treatment resistance (OR 2.85; 95% CI: 1.06-2.86; p = 0.036). Additionally, elevated serum creatinine level, with each increment of 150 μmol/l, predicted resistance (p = 0.002). Among the 74 patients where remission was achieved, Cox proportional hazards models detected that those with PR3 ANCA were 1.31 times more likely to experience a relapse than were patients with MPO ANCA (95% CI: 1.01-5.35; p = 0.0001). Lung involvement was associated with an increased risk of relapse (HR 1.87; 95% CI: 1.12-4.35; p = 0.014). Although not significant, advanced age tended to be associated with relapse (p = 0.08). CONCLUSION: Our findings highlight the important effect of female sex and severity of renal disease at presentation as predictors of treatment resistance, and PR3 ANCA and lung involvement as predictors of relapse.
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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 | 0.000 | 0.002 |
| 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".