What are the keys to successful adrenal venous sampling (AVS) in patients with primary aldosteronism?
Bibliographic record
Abstract
Adrenal venous sampling (AVS) is the criterion standard to distinguish between unilateral and bilateral adrenal disease in patients with primary aldosteronism. The keys to successful AVS include appropriate patient selection, careful patient preparation, focused technical expertise, defined protocol, and accurate data interpretation. The use of AVS should be based on patient preferences, patient age, clinical comorbidities, and the clinical probability of finding an aldosterone-producing adenoma. AVS is optimally performed in the fasting state in the morning. AVS is an intricate procedure because the right adrenal vein is small and may be difficult to locate - the success rate depends on the proficiency of the angiographer. The key factors that determine the successful catheterization of both adrenal veins are experience, dedication and repetition. With experience, and focusing the expertise to 1 or 2 radiologists at a referral centre, the AVS success rate can be as high as 96%. A centre-specific, written protocol is mandatory. The protocol should be developed by an interested group of endocrinologists, radiologists and laboratory personnel. Safeguards should be in place to prevent mislabelling of the blood tubes in the radiology suite and to prevent sample mix-up in the laboratory.
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".