Application of strict criteria in adrenal venous sampling increases the proportion of missed patients with unilateral disease who benefit from surgery for primary aldosteronism
Bibliographic record
Abstract
OBJECTIVES: Adrenal vein sampling (AVS) is intended to confirm unilateral forms of primary aldosteronism, which are amenable to surgical cure. Excessively strict AVS criteria to define lateralization may result in many patients incorrectly categorized as bilateral primary aldosteronism and opportunity for surgical cure missed. METHODS: Retrospective review of an AVS-primary aldosteronism database in which surgical cases are verified by standardized outcomes. Having used 'less strict' AVS criteria for lateralization, we examined the distribution of AVS lateralization indices in our confirmed unilateral primary aldosteronism cases both with and without cosyntropin stimulation. The proportion of proven unilateral cases that would have been missed with stricter AVS interpretation criteria was calculated. Particular focus was given to the proportion of missed cases according to use of international guidelines. False-positive lateralization with 'less strict' interpretation was also calculated. RESULTS: Of 80 surgical primary aldosteronism cases, 10-23% would have been missed with AVS lateralization indices of 3 : 1 to 5 : 1, with or without cosyntropin. If strict selectivity indices (for confirmation of catheterization) were combined with strict lateralization indices, up to 70% of unilateral primary aldosteronism cases could have been missed. Use of Endocrine Society AVS guidelines would have missed 21-43% of proven unilateral cases. 'Less strict' AVS interpretation yielded one case (1.2%) of false lateralization. CONCLUSION: Excessively strict AVS interpretation criteria will result in a high rate of missed unilateral primary aldosteronism with subsequent loss of opportunity for intervention. Use of more lenient lateralization criteria will improve the detection rate of unilateral primary aldosteronism with very low false-positive rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".