Hair cortisol content in patients with adrenal insufficiency on hydrocortisone replacement therapy
Bibliographic record
Abstract
OBJECTIVE: Patients with adrenal insufficiency (AI) require life-long replacement therapy with exogenous glucocorticoids. Several studies have shown impaired subjective health status in these patients as well as increased morbidity and mortality risk, which may be caused by glucocorticoid over-replacement. As a measure of long-term cortisol exposure, the usefulness of hair cortisol analysis in patients receiving glucocorticoid replacement therapy was investigated. PATIENTS AND DESIGN: Hair samples, demographics, medical history and perceived stress scale questionnaires were collected from 93 patients across North America diagnosed with primary or secondary AI. Sixty-two household partners served as a control group. Cortisol was measured in the proximal 2 cm of hair, representing the most recent 2 months of exposure. A modified enzyme immunoassay was used for the measurement of cortisol. RESULTS: The male patients had significantly higher hair cortisol levels than the male controls (P < 0·05), while there was no significant difference among females. Hair cortisol content correlated significantly with glucocorticoid dose (r = 0·3, P < 0·01). Patients with AI had significantly higher subjective stress scores than control subjects. CONCLUSIONS: Hair cortisol content correlates with hydrocortisone (HC) dose in patients with AI. Our results suggest that some AI patients may be over-treated and hence may be at risk for the adverse effects of cortisol. Measurement of HC in hair may become a useful monitoring tool for long-term cortisol exposure in patients treated with glucocorticoids.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".