Characteristics and Medium-term Outcomes of Takayasu Arteritis–related Renal Artery Stenosis: Analysis of a Large Chinese Cohort
Bibliographic record
Abstract
Objective. To investigate the characteristics of patients with Takayasu arteritis (TA)-related renal artery stenosis and identify the predictors of medium-term adverse outcomes. Methods. Data for 567 patients registered in the East China Takayasu arteritis cohort, a large prospective observational cohort, up to April 30, 2019, were retrospectively analyzed. Results. Renal artery stenosis was confirmed in 172/567 (30.34%) patients, with left renal artery involvement seen in 73/172 (42.44%) patients. Renal insufficiency at presentation (HR 2.37, 95% CI 1.76–15.83, P = 0.03), bilateral renal artery involvement (HR 6.95, 95% CI 1.18–21.55, P = 0.01), and severe stenosis (> 75%; HR 4.75, 95% CI 1.08–11.33, P = 0.05) were predictors of adverse outcomes. A matrix model constructed using 3 variables (renal function, stenosis severity, and bilateral renal artery involvement) could identify 3 risk groups. Revascularization was performed for 46 out of 172 (26.74%) patients. Patients without preoperative treatment had higher rate of restenosis (41.46% vs 16.67%, P < 0.01) and worsening hypertension (25.93% vs. 10.53%, P < 0.01) after the procedure. Nonreceipt of preoperative treatment (HR 6.5, 95% CI 1.77–32.98, P = 0.04) and active disease at revascularization (HR 4.21, 95% CI 2.01–21.44, P = 0.04) were independent predictors of adverse outcomes after revascularization. Conclusion. Patients with TA-associated renal artery stenosis and uncontrolled or worsening hypertension or/and renal function may benefit from revascularization. Those who have received preoperative treatment may have more favorable revascularization outcomes. Prognosis appears to be poorer for patients with renal insufficiency at presentation, bilateral artery involvement, and severe stenosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".