Screening Tests for Hypercortisolism in Patients With Adrenal Incidentaloma
Bibliographic record
Abstract
Background: To compare the diagnostic performance of different first-line screening tests for subclinical hypercortisolism (SH) in patients with adrenal incidentaloma (AI). Methods: We studied a series of patients with AI, with no clinical evidence of hormonal hypersecretion. For screening for SH, all patients performed 1-mg dexamethasone suppression test (1-mg DST), late night salivary cortisol (LNSC) and 24-h urinary free cortisol (UFC). A control group of patients with confirmed Cushing’s syndrome (CS) was used to calculate the diagnostic performance of the screening tests. Results: In the 83 patients with AI, morning cortisol after 1-mg DST was <= 1.8 ? g/dL in 69.9%, 1.9 to 5 ? g/dL in 26.5% and > 5 ? g/dL in 3.6%. LNSC was elevated in 20.5% and all patients had normal UFC levels. In the control group, composed of 50 patients with confirmed CS, all patients who underwent 1-mg DST had cortisol levels > 1.8 ? g/dL (1.9 to 5 ? g/dL in 16.2% and > 5 ? g/dL in 83.3%); LNSC was elevated in 93.8% and the UFC was increased in 85.4% of patients tested. Overall, for the screening of SH, the 1-mg DST presented a sensitivity and specificity of 100% and 69.9% with its lowest threshold (<= 1.8 ? g/dL) and 83.3% and 96.4% with its highest threshold (< 5 ? g/dL). LNSC showed a sensitivity and specificity of 93.8% and 79.5% and the UFC of 85.4% and 100%, respectively. Conclusions: The 1-mg DST at its lowest threshold presented the highest sensitivity in identifying SH, but its low specificity encourages us to consider UFC levels, to reduce false-positive test results. J Endocrinol Metab. 2018;8(4):62-68 doi: https://doi.org/10.14740/jem510w
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".