Subtyping of Primary Aldosteronism in the AVIS-2 Study: Assessment of Selectivity and Lateralization
Bibliographic record
Abstract
CONTEXT: Adrenal venous sampling (AVS) is the key test for subtyping primary aldosteronism (PA), but its interpretation varies widely across referral centers and this can adversely affect the management of PA patients. OBJECTIVES: To investigate in a real-life study the rate of bilateral success and identification of unilateral aldosteronism and their impact on blood pressure outcomes in PA subtyped by AVS. DESIGN AND SETTINGS: In a retrospective analysis of the largest international registry of individual AVS data (AVIS-2 study), we investigated how different cut-off values of the selectivity index (SI) and lateralization index (LI) affected rate of bilateral success, identification of unilateral aldosteronism, and blood pressure outcomes. RESULTS: AVIS-2 recruited 1625 individual AVS studies performed between 2000 and 2015 in 19 tertiary referral centers. Under unstimulated conditions, the rate of biochemically confirmed bilateral AVS success progressively decreased with increasing SI cut-offs; furthermore, with currently used LI cut-offs, the rate of identified unilateral PA leading to adrenalectomy was as low as <25%. A within-patient pairwise comparison of 402 AVS performed both under unstimulated and cosyntropin-stimulated conditions showed that cosyntropin increased the confirmed rate of bilateral selectivity for SI cut-offs ≥ 2.0, but reduced lateralization rates (P < 0.001). Post-adrenalectomy outcomes were not improved by use of cosyntropin or more restrictive diagnostic criteria. CONCLUSION: Commonly used SI and LI cut-offs are associated with disappointingly low rates of biochemically defined AVS success and identified unilateral PA. Evidence-based protocols entailing less restrictive interpretative cut-offs might optimize the clinical use of this costly and invasive test. (J Clin Endocrinol Metab XX: 0-0, 2020).
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".