Use of late-night salivary cortisol to monitor response to medical treatment in Cushing’s disease
Bibliographic record
Abstract
OBJECTIVE: Monitoring of patients with Cushing's disease on cortisol-lowering drugs is usually performed with urinary free cortisol (UFC). Late-night salivary cortisol (LNSC) has an established role in screening for hypercortisolism and can help to detect the loss of cortisol circadian rhythm. Less evidence exists regarding the usefulness of LNSC in monitoring pharmacological response in Cushing's disease. DESIGN: Exploratory analysis evaluating LNSC during a Phase III study of long-acting pasireotide in Cushing's disease (clinicaltrials.gov: NCT01374906). METHODS: Mean LNSC (mLNSC) was calculated from two samples, collected on the same days as the first two of three 24-h urine samples (used to calculate mean UFC [mUFC]). Clinical signs of hypercortisolism were evaluated over time. RESULTS: At baseline, 137 patients had evaluable mLNSC measurements; 91.2% had mLNSC exceeding the upper limit of normal (ULN; 3.2 nmol/L). Of patients with evaluable assessments at month 12 (n = 92), 17.4% had both mLNSC ≤ULN and mUFC ≤ULN; 22.8% had mLNSC ≤ULN, and 45.7% had mUFC ≤ULN. There was high variability in LNSC (intra-patient coefficient of variation (CV): 49.4%) and UFC (intra-patient CV: 39.2%). mLNSC levels decreased over 12 months of treatment and paralleled changes in mUFC. Moderate correlation was seen between mLNSC and mUFC (Spearman's correlation: ρ = 0.50 [all time points pooled]). Greater improvements in systolic/diastolic blood pressure and weight were seen in patients with both mLNSC ≤ULN and mUFC ≤ULN. CONCLUSION: mUFC and mLNSC are complementary measurements for monitoring treatment response in Cushing's disease, with better clinical outcomes seen for patients in whom both mUFC and mLNSC are controlled.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".