A Multi‐institutional Comparison of Adrenal Venous Sampling in Patients with Primary Aldosteronism: Caution Advised if Successful Bilateral Adrenal Vein Sampling is Not Achieved
Bibliographic record
Abstract
INTRODUCTION: In patients with primary aldosteronism (PA), adrenal venous sampling (AVS) is recommended to differentiate between unilateral (UNI) or bilateral (BIL) adrenal disease. A recent study suggested that lateralization could be predicted, based on the ratio of aldosterone/cortisol levels (A/C) between the left adrenal vein (LAV) and inferior vena cava (IVC), with a 100% positive predictive value (PPV). This study aimed to validate those findings utilizing a larger, multi-institutional cohort. METHODS: A retrospective review was performed of patients with PA who underwent AVS from 2 tertiary-care institutions. Laterality was predicted by an A/C ratio of >3:1 between the dominant and non-dominant adrenal. AVS results were compared to LAV/IVC ratios utilizing the published criteria (Lt ≥ 5.5; Rt ≤ 0.5). RESULTS: Of 222 patients, 124 (57%) had UNI and 98 (43%) had BIL disease based on AVS. AVS and LAV/IVC findings were concordant for laterality in 141 (64%) patients (69 UNI, 72 BIL). Using only the LAV/IVC ratio, 54 (24%) patients with UNI disease on AVS who underwent successful surgery would have been assumed to have BAH unless AVS was repeated, and 24 (11%) patients with BIL disease on AVS may have been incorrectly offered surgery (PPV 70%). Based on median LAV/IVC ratios (left 5.26; right 0.31; BIL 2.84), no LAV/IVC ratio accurately predicted laterality. DISCUSSION: This multi-institutional study of patients with both UNI and BIL PA failed to validate the previously reported PPV of LAV/IVC ratio for lateralization. Caution should be used in interpreting incomplete AVS data to differentiate between UNI versus BIL disease and strong consideration given to repeat AVS prior to adrenalectomy.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".