Imaging of Renovascular Hypertension: Respective Values of Renal Scintigraphy, Renal Doppler US, and MR Angiography
Bibliographic record
Abstract
Renovascular hypertension affects 15%-30% of patients who have clinical criteria suggestive of renovascular disease. Noninvasive screening is crucial for patient selection prior to conventional angiography and renal revascularization. Renal scintigraphy has been reported to be sensitive for detection of renovascular hypertension, but some of its limitations (eg, in the setting of bilateral renal artery stenosis and renal failure) should be considered. Doppler ultrasonography (US) allows direct evaluation of the renal arteries as well as transrenal Doppler waveform analysis, but it remains operator dependent. Gadolinium-enhanced magnetic resonance (MR) angiography is becoming an excellent alternative to conventional angiography. The main limiting factors of this technique are inadequate visualization of segmental and accessory renal arteries as well as a tendency toward overestimation of stenoses. Given the high cost and low availability of MR angiography, scintigraphy and Doppler US should be considered the primary studies in screening for renovascular hypertension. MR angiography could be reserved for patients with inconclusive scintigraphic and Doppler US results, patients with high clinical suspicion of renovascular hypertension, and patients with a contraindication to conventional angiography.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".