Adrenal incidentalomas and subclinical Cushing's syndrome: diagnosis and treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: Adrenal incidentaloma has become a frequent clinical dilemma. Even in the absence of specific clinical features of Cushing's syndrome, adrenocortical incidentalomas may display steroid secretory activity at different degrees. The recognition of endocrine and metabolic dysfunctions associated with subclinical hypercortisolism leads to current awareness about its potential consequences. RECENT FINDINGS: Different protocols and threshold values to define normal cortisol secretion and diagnosis of subclinical Cushing's syndrome have been proposed, including recent practice guidelines for the diagnosis of overt Cushing's syndrome. Follow-up studies have provided additional data about the natural course of the disease and related cardiovascular and metabolic consequences. The study of bilateral adrenocorticotropin-independent macronodular adrenocortical hyperplasia in some familial cases offers a new approach to understanding the spectrum of subclinical cortisol hypersecretion. SUMMARY: The prevalence of subclinical hypercortisolism may be higher than previously reported as more sensitive diagnostic criteria are now recommended. The absence of a single gold standard test, the diversity of diagnostic criteria and the requirement of subsequent meticulous biochemical evaluations before a decision for treatment represent a challenge for the clinical management of this condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".