Multiple radiologist review of adrenal CT still frequently misses lateralized surgical primary aldosteronism
Bibliographic record
Abstract
Patients with primary aldosteronism (PA) have increased morbidity and mortality compared to those with essential hypertension. Accurate detection of lateralized PA is important so that affected patients can receive potentially curative adrenalectomy. However, around 40% of patients with lateralized PA have "normal" adrenal glands on computed tomography (CT). Additional independent review of imaging has been shown to improve diagnostic accuracy in many areas of imaging. Therefore, the authors sought to establish if multi-reader re-assessment of previously reported normal CT scans would result in increased detection of surgically remediable disease. The authors found that re-assessment of CT imaging by one, two, or three additional radiologists (or a combination thereof) slightly increased the detection of lateralized disease, but these differences were not statistically significant (p > .05). Readers had low inter-observer agreement (kappa = 0.17). If detection of a discrete nodule on CT was made a prerequisite for adrenal vein sampling (AVS), a second read by another reviewer would still result in an excess of missed cases (84.2%, 36.8%, and 65.8%, respectively, for each of the three independent reviewers). Therefore, a "normal" CT does not preclude the possibility of lateralized PA. Adrenal vein sampling should still be strongly considered wherever available and whenever surgery is considered for treatment of PA, irrespective of CT findings.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".