Apparent failed and discordant adrenal vein sampling: A potential confounding role of cortisol cosecretion?
Bibliographic record
Abstract
OBJECTIVE: Adrenal vein sampling (AVS) and computed tomography (CT) often show confusingly discordant lateralisation results in primary aldosteronism (PA). We tested a biochemical algorithm using AVS data to detect cortisol cosecretion as a potential explanation for discordant cases. DESIGN: Retrospective analysis from a large PA + AVS database. PATIENTS: All patients with PA and AVS, 2005-2020. MEASUREMENTS: An algorithm using biochemical data from paired AVS + CT images was devised from physiological first principles and informed by data from unilateral, AVS-CT concordant patients. The algorithm involved calculations based upon the expectation that low cortisol levels exist in adrenal vein effluent opposite an aldosterone-and-cortisol-producing adrenal mass and may reverse lateralisation due to inflated aldosterone/cortisol ratios. MAIN OUTCOMES: The algorithm was applied to cases with discordant CT-AVS lateralisation to determine whether this might be a common or explanatory finding. Clinical and biochemical characteristics of identified cases were collected via chart review and compared to CT-AVS concordant cases to detect evidence of biological plausibility for cortisol cosecretion. RESULTS: From a total of 588 AVS cases, 141 AVS + CT pairs were clear unilateral PA cases, used to develop the three-step algorithm for AVS interpretation. Applied to 88 AVS + CT discordant pairs, the algorithm suggested possible cortisol cosecretion in 40%. Case review showed that the proposed cortisol cosecretors, as identified by the algorithm, had low/suppressed adrenocorticotropic hormone levels, larger average nodule size and lower plasma aldosterone. CONCLUSIONS: Pending external validation and outcome verification by surgery and tissue immunohistochemistry, cortisol cosecretion from aldosteronomas may be a common explanation for discordant CT-AVS results in PA.
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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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".