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 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 | 0.032 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".