Adrenal infections update: how radiologists can contribute to patient care
Bibliographic record
Abstract
Adrenal infections are considered clinically important but often go unrecognized, with a significant number of cases only diagnosed post-mortem. The limited evidence regarding imaging findings in the literature emphasizes the need to detect and diagnose these infections early in disease course to improve patient outcomes. A range of microorganisms, including fungi, viruses, parasites, and bacteria, can directly or indirectly affect the morphology and function of the adrenal glands. When evaluating a patient with adrenal infection, several immunological and hormonal factors should be considered, such as the status of the hypothalamic-pituitary-adreno cortical axis and the serum cortisol level. Moreover, certain microorganisms specifically target one of the zones of the adrenal glands or vascular supply, resulting in distinct imaging manifestations. The purpose of this article is to describe the fundamental clinical features and imaging manifestations associated with adrenal infections, enabling radiologists to make informed interpretations and contribute to accurate diagnostic assessments.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".