New Cutoffs for the Biochemical Diagnosis of Adrenal Insufficiency after ACTH Stimulation using Specific Cortisol Assays
Bibliographic record
Abstract
CONTEXT: ) is considered to be ≥18 μg/dL (500 nmol/L). This threshold is based on older serum cortisol assays. Specific monoclonal antibody immunoassays or LC-MS/MS may have lower thresholds for a normal response. OBJECTIVE: To calculate serum cortisol cutoff values for adrenocorticotropic hormone (ACTH) stimulation testing with newer specific cortisol assays. METHODS: Retrospective analysis of ACTH stimulation tests performed in ambulatory and hospitalized patients suspected of adrenal insufficiency (AI). Serum samples were assayed for cortisol in parallel using Elecsys I and Elecsys II immunoassays, and when volume was available, by Access immunoassay and LC-MS/MS. RESULTS: A total of 110 patients were evaluated. Using 18 μg/dL as the cortisol cutoff after ACTH stimulation, 14.5%, 29%, 22.4%, and 32% of patients had a biochemical diagnosis of AI using the Elecsys I, Elecsys II, Access, and LC-MS/MS assays, respectively. Deming regressions of serum cortisol were used to calculate new cortisol cutoffs based on the Elecsys I cutoff of 18 μg/dL. For 30-minute values, new cutoffs were 14.6 μg/dL for Elecsys II, 14.8 μg/dL for Access, and 14.5 μg/dL for LC-MS/MS. Baseline cortisol <2 μg/dL was predictive of subnormal stimulated cortisol values. CONCLUSION: To reduce false positive ACTH stimulation testing, we recommend a new serum cortisol cutoff of 14 to 15 μg/dL depending on the assay used (instead of the historical value of 18 μg/dL with older polyclonal antibody assays). Clinicians should be aware of the new cutoffs for the assays available to them when evaluating patients for AI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".