Discordance Between Imaging and Adrenal Vein Sampling in Primary Aldosteronism Irrespective of Interpretation Criteria
Bibliographic record
Abstract
BACKGROUND: Subtyping of primary aldosteronism (PA) using imaging and adrenal vein sampling (AVS) may yield discordant results, causing confusion in management. Interpretation criteria for AVS lateralization may affect discordance rates. METHODS: We identified consecutive patients with PA who underwent AVS at a quaternary care center between January 2006 and May 2018. Patient demographics, laboratory results, diagnostic imaging, and AVS results were retrieved. Adrenal cross-sectional imaging was compared with AVS findings. The presence of lateralization was defined using varying thresholds for the lateralization index (LI) from >2:1 to >5:1. Discordance was defined by a unilateral lesion on imaging with contralateral or nonlateralization on AVS. RESULTS: A total of 342 patients were included; 68.7% had hypokalemia. With cross-sectional imaging, 191 (55.6%) patients had unilateral lesions, 47 (13.7%) had bilateral lesions, and 104 (30.4%) had normal imaging. Overall discordance rates were high, ranging from 22% to 28% for LI thresholds of >2:1 and >5:1, respectively. Discordance between imaging and AVS was positively correlated with LI threshold stringency (P < 0.001). Patients with normal or bilateral lesions on imaging frequently lateralized on AVS. Lateralization, when present, was approximately equal between left and right sides, irrespective of the LI threshold. CONCLUSIONS: Discrepancies between imaging and AVS were common, even among patients with nonspecific imaging. Discordance was greatest with the strictest AVS interpretation criteria. Even under the most lenient thresholds, apparent discordance between imaging and AVS exceeded 20% and may limit the ability to make surgical decisions. Reliance on imaging alone for detecting lateralization may be misleading.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".