Therapeutic Potential of Intranasal Corticosteroids for Chronic Cough
Bibliographic record
Abstract
Background: Chronic cough, a condition defined as a cough persisting for more than 8 weeks, remains a significant clinical challenge with a considerable impact on quality of life. Intranasal corticosteroids (INCS) are widely recommended in clinical guidelines for managing chronic cough, particularly in patients with associated upper airway conditions. However, the evidence base directly supporting this practice is surprisingly sparse, leaving clinicians to navigate a disconnect between guidelines and real-world applicability. Objective: This article offers a critical perspective on the role of INCS in chronic cough management, drawing attention to the paucity of direct evidence and proposing a roadmap for future research. Discussion: A recent systematic review aiming to evaluate the efficacy and safety of INCS for chronic cough yielded no eligible studies, despite extensive database searches. This unexpected outcome highlights a major gap in the literature and raises important questions about the foundation of current guideline recommendations. While INCS are biologically plausible and have demonstrated efficacy in related conditions, such as allergic rhinitis and chronic rhinosinusitis, their specific role in chronic cough remains unverified. The lack of robust clinical trials underscores the need for targeted research to determine whether INCS provide meaningful benefit in this population. Conclusion: The disconnect between recommendations and evidence in chronic cough management underscores a critical need for well-designed randomized controlled trials. Until such data are available, clinicians must balance existing guidelines with clinical judgment, individualizing treatment to address the unique needs of their patients. Bridging this evidence gap will not only enhance patient care but also refine guideline development, ensuring recommendations are firmly grounded in high-quality research.
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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 | 0.001 | 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 | 0.001 |
| 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 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".