Hypothalamic‐pituitary‐adrenal axis suppression and intranasal corticosteroid use: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Intranasal corticosteroids (INCS) are used in the management of sinonasal conditions. Use of exogenous steroids can be associated with hypothalamic-pituitary-adrenal axis dysfunction and adrenal insufficiency (AI). We aimed to estimate the rate of AI after INCS use in a meta-analysis, stratified by steroid type and treatment duration. METHODS: Ovid Medline, Embase Classic, PubMed, Web of Science, and CINAHL databases were searched following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify studies investigating INCS use and AI. AI was defined as morning serum cortisol <550 nmol/L and <80 nmol/L with and without adrenocorticotropic hormone stimulation. INCS were classified as first (beclomethasone dipropionate, triamcinolone acetonide, beclomethasone, budesonide, dexamethasone) and second (ciclesonide, mometasone furoate, and fluticasone propionate) generation. Duration of treatment was classified as short (<1 month), medium (1-12 months), and long-term (>12 months) time periods. RESULTS: This search identified 3668 articles. A total of 39 studies (1678 patients) were included in the final analysis. The pooled percentage of AI for routinely utilized first- and second-generation INCS was 0.70% (95% confidence interval [CI], 0.29-1.12%). Stratified by type, AI was observed in 0.78% (95% CI, 0.25-1.30%) of first-generation and 0.58% (95% CI, -0.1% to 1.26%) of second-generation steroids. AI was seen in 0.48% (95% CI, -0.01% to 0.96%) of short-term, 1.13% (95% CI, 0.2-2.1%) of medium-term, and 1.67% (95% CI, 0.37-2.9%) of long-term use of INCS. CONCLUSION: Overall, the use of INCS carries a low risk for AI. Although modest, this risk may differ depending on the length of duration and type of INCS used. Informing patients of these risks is of importance for the treatment of chronic sinonasal conditions.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".