Despite Limited Specificity, Computed Tomography Predicts Lateralization and Clinical Outcome in Primary Aldosteronism
Bibliographic record
Abstract
BACKGROUND: Computed tomography (CT) of the adrenals is a common first step for investigation of primary aldosteronism (PA). However, prior studies report poor specificity, necessitating adrenal vein sampling (AVS) prior to surgical consideration. METHODS: We examined our AVS database to determine whether CT adrenal findings could help select patients with a high likelihood of lateralization by AVS or high-value blood pressure (BP) outcomes. Subjects (N = 113) with validated outcomes were divided into groups of CT 'positive' or CT 'negative' according to the presence or absence of an adrenal mass and compared for the outcomes of lateralization by AVS or proportions achieving normotension off medications following surgery. RESULTS: For patients with CT adrenal masses, there was a significantly higher odds ratio (OR) for both outcomes (6.3 and 9.7, p < 0.01). In subgroup analysis, age <40 years carried particularly high odds for lateralization and cure when a CT mass was present (ORs 45 and 26, p < 0.01). Young individuals with normal CT adrenals rarely lateralized (10 %) and, in such patients, even factors like hypokalemia, body mass index (BMI), and plasma aldosterone level did not change the result on regression analysis. CONCLUSIONS: CT-imaged adrenal masses strongly predicted lateralization by AVS and normotension with surgical treatment of lateralized PA. In PA, CT-positive patients should indeed be offered AVS and/or surgery given the high chance of good outcomes; younger CT-negative patients should be advised of a low chance of finding surgical disease by AVS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".