FP091A POSITIVE RENAL VEIN RENIN LATERALISATION RATIO INDEPENDENTLY PREDICTS BLOOD PRESSURE BENEFIT FROM ANGIOPLASTY IN RENAL ARTERY STENOSIS: A RETROSPECTIVE STUDY
Bibliographic record
Abstract
Introduction and Aims: Recent randomized trials failed to demonstrate greater benefit from angioplasty compared to medical treatment of renal artery stenosis (RAS). However, not all RAS are hemodynamically significant and identification of patients likely to benefit from angioplasty remains essential. In this regard, renal vein renin studies remain controversial and we examined whether performing them under stringent conditions might predict blood pressure (BP) improvement. Methods: Patients with RAS ≥ 60% who underwent renal vein renin studies in 2008-2013 were identified. Before the renal vein renin measurements, agents known to affect renin levels were withdrawn and replaced with non-interfering drugs. After a 5 days admission with a < 40 mmol/d sodium diet, patients were kept in a recumbent position from midnight and until the end of the procedure. Simultaneous renal and peripheral veins renin samples were collected before and after stimulation with enalaprilat/captopril. Lateralisation ratios (RVRR) were calculated by dividing renin levels ipsilateral to the RAS with contralateral levels and a positivity was ≥ 1.5. Beneficial clinical response was defined as BP < 140/90 mmHg without medication, 10% decreased mean BP without increased daily defined doses (DDD) or decreased DDD without significant increase of mean BP. Variables associated with clinical response were analysed by logistic regression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".