UNILATERALLY SELECTIVE ADRENAL VEIN SAMPLING FOR IDENTIFICATION OF SURGICALLY CURABLE PRIMARY ALDOSTERONISM
Bibliographic record
Abstract
Objective: Adrenal venous sampling (AVS) is recommended for the identification of unilateral surgically curable primary aldosteronism (PA) but, owing to failed bilateral adrenal vein cannulation, is often clinically useless. Design and method: We investigated if only unilaterally selective AVS studies can allow identification of the responsible adrenal. Methods: Among 1625 patients consecutively submitted to AVS in tertiary referral centers, we selected those with a unilaterally selective AVS result and a final diagnosis of unilateral PA. Using the latter as gold reference, we examined the accuracy of the relative aldosterone secretion index (RASI), which estimates the amount of aldosterone produced in each adrenal gland corrected for catheterization selectivity. Results: The RASI values distribution showed prominent differences between patients with and without unilateral PA. The area under ROC curves (AUROC) was 0.714 and 0.855 for RASI values of the responsible and the contralateral side, respectively. While RASI values >2.55 on the culprit side and less than 0.96 on the contralateral side furnished the highest accuracy for detection of surgically cured unilateral PA, only 20% and 16% had RASI values less than 0.96 and >2.55, respectively, in the patients without unilateral PA. Conclusions: With the strength of the gold standard entailing an unambiguous diagnosis of unilateral PA and large real-life dataset, these results indicate the feasibility of identifying unilateral PA using unilaterally selective AVS results.
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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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".