Incidental Atherosclerotic Renal Artery Stenosis in Patients Undergoing Elective Coronary Angiography: Are These Lesions Significant?
Bibliographic record
Abstract
BACKGROUND: Cardiologists often identify atherosclerotic renal artery stenosis (ARAS) during cardiac angiography. The importance of such 'incidental' ARAS (iARAS) is not known. The present study sought to describe renal perfusion using non-captopril (baseline) nuclear renograms in patients with iARAS, and to determine characteristics associated with a positive captopril renogram. METHODS: Patients presenting for non-emergent coronary angiography between June 2001 and February 2006 were angiographically screened for iARAS. Those with >50% stenosis of one or both renal arteries were referred to nephrology and underwent nuclear renography. RESULTS: 131 patients had renograms. The mean age was 73.2 +/-8.1 and median eGFR was 51.2 (40.0, 66.6) ml/min/1.73 m(2). 51% had evidence of reduced perfusion to one kidney, of which 13% were discordant with the angiographic lesion. 9% had positive captopril renograms. Captopril renogram positivity was associated with severe unilateral stenosis (p = 0.02). CONCLUSIONS: In cardiac patients diagnosed with iARAS, the presence of known anatomic lesions did not correlate with captopril renogram positivity. Uncertainty remains as to whether nuclear renography is a poor functional test in this population, or the lesions are not functionally significant. These results lead us to question both the significance of such lesions, and the utility of conducting renograms in this population.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".