Evaluation and Management of Glucocorticoid-Induced Adrenal Insufficiency in IBD: An Expert Opinion
Bibliographic record
Abstract
BACKGROUND AND AIMS: Glucocorticoid-induced adrenal insufficiency (GC-AI) is a potentially life-threatening side effect of glucocorticoid therapy. Currently, there is no consensus on monitoring and treating GC-AI in inflammatory bowel disease (IBD) patients. This systematic review and meta-analysis aimed to determine the prevalence of GC-AI in IBD patients following glucocorticoid use. Additionally, a Delphi panel was conducted to develop evidence-based expert opinions on evaluating and managing GC-AI in IBD patients. METHODS: Thirty-four articles were included in this study. Of these, 26 articles reported the prevalence of GC-AI in IBD patients. Statements were generated and rated by a panel of adult and pediatric gastroenterologists using a 1-9 scale. Statements were classified as inappropriate, uncertain, or appropriate based on the median panel rating and the degree of disagreement. RESULTS: The prevalence of GC-AI across all studies was 26.9% (95% CI: 18.9-36.8, I2: 96%). The panel emphasized the importance of maintaining a high suspicion for GC-AI in IBD patients treated with systemic glucocorticoids and considering risk factors such as exogenous glucocorticoid use ≥4 weeks at doses ≥5 mg of prednisone-equivalent. Recommendations for initial screening and management of GC-AI are provided. The management of GC-AI in special populations, such as those in the perioperative setting is also addressed. The panel underscored the need to consider GC-AI assessment in clinical trial design. CONCLUSIONS: GC-AI is a serious, often underrecognized side effect of glucocorticoid use. This study presents expert opinions on the evaluation and management of GC-AI in IBD patients, emphasizing the need for vigilance and appropriate management strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".