Expert Consensus on the Primary Aldosteronism Severity Classification and its strategic application in indicating adrenal venous sampling
Bibliographic record
Abstract
OBJECTIVE: Severity classifications are essential for many diseases to prioritize patient management tasks such as diagnosis, treatment, and follow-up. Primary aldosteronism (PA), a common cause of secondary hypertension, lacks a standardized severity scale despite generally requiring invasive diagnostics like adrenal venous sampling (AVS). This study aimed to develop a global expert consensus-based classification for PA severity to improve clinical decision-making. METHODS: A panel of 45 international experts from 40 centers across four continents used the Delphi method to create a consensus severity classification for PA. This classification was then applied retrospectively to 2593 PA patients from 26 centers to assess its association with the disease subtype. RESULTS: After four rounds, the Primary Aldosteronism Severity Classification (PASC), which integrates biochemical and clinical parameters including serum potassium, blood pressure, and basal plasma aldosterone concentration, was established. Primary Aldosteronism Severity Classification classifies PA into mild (3 and 4 points), moderate (5-7 points), and severe (8 and 9 points). Among the cohort from 26 centers, 13.9%, 63.0%, and 23.1% were classified as mild, moderate, and severe, respectively, aligning with lateralized subtype prevalence rates of 14.7%, 44.6%, and 72.6%. CONCLUSION: Primary Aldosteronism Severity Classification is a newly developed simplified, semi-quantitative classification of PA severity. The correlation between PASC and lateralized PA subtype supports its potential to provide graded recommendations of AVS prior to surgical indication in each patient.
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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 | 0.121 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".