Role of Adrenal Vein Sampling in Guiding Surgical Decision in Primary Aldosteronism
Bibliographic record
Abstract
Adrenal vein sampling (AVS) is recommended for subtyping primary aldosteronism (PA) to identify lateralized or bilateral sources of aldosterone excess, allowing for better decision-making in regard to medical or surgical management on a case-by-case basis. To date, no consensus exists on protocols to be used during AVS, especially concerning sampling techniques, the timing of sampling, and whether or not to use adrenocorticotropic hormone (ACTH) stimulation. Interpretation criteria for selectivity, lateralization, and contralateral suppression vary from one expert center to another, with some favoring strict cut-offs to others being more permissive. Clinical and biochemical post-operative outcomes can also be influenced by AVS criteria utilized to indicate surgical therapy.In this review, we reanalyze studies on AVS highlighting the recent pathological findings of frequent micronodular hyperplasia adjacent to a dominant aldosteronoma (APA) overlapping with bilateral idiopathic hyperaldosteronism (IHA) etiologies, as opposed to the less frequent unilateral single aldosteronoma. The variable expression of melanocortin type 2 receptors in the nodules and hyperplasia may explain the frequent discordance in lateralization ratios between unstimulated and ACTH- stimulated samples. We conclude that aldosterone values collected during simultaneous bilateral sampling, both at baseline and post-ACTH stimulation, are required to adequately evaluate selectivity, lateralization, and contralateral suppression during AVS, to better identify all patients with PA that can benefit from a surgical indication. Recommended cut-offs for each ratio are also presented.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".