Treatment of Unexplained Chronic Cough
Bibliographic record
Abstract
BACKGROUND: Unexplained chronic cough (UCC) causes significant impairments in quality of life. Effective assessment and treatment approaches are needed for UCC. METHODS: This systematic review of randomized controlled trials (RCTs) asked: What is the efficacy of treatment compared with usual care for cough severity, cough frequency, and cough-related quality of life in patients with UCC? Studies of adults and adolescents aged > 12 years with a chronic cough of > 8 weeks' duration that was unexplained after systematic investigation and treatment were included and assessed for relevance and quality. Based on the systematic review, guideline suggestions were developed and voted on by using the American College of Chest Physicians organization methodology. RESULTS: Eleven RCTs and five systematic reviews were included. The 11 RCTs reported data on 570 participants with chronic cough who received a variety of interventions. Study quality was high in 10 RCTs. The studies used an assortment of descriptors and assessments to identify UCC. Although gabapentin and morphine exhibited positive effects on cough-related quality of life, only gabapentin was supported as a treatment recommendation. Studies of inhaled corticosteroids (ICS) were affected by intervention fidelity bias; when this factor was addressed, ICS were found to be ineffective for UCC. Esomeprazole was ineffective for UCC without features of gastroesophageal acid reflux. Studies addressing nonacid gastroesophageal reflux disease were not identified. A multimodality speech pathology intervention improved cough severity. CONCLUSIONS: The evidence supporting the diagnosis and management of UCC is limited. UCC requires further study to establish agreed terminology and the optimal methods of investigation using established criteria for intervention fidelity. Speech pathology-based cough suppression is suggested as a treatment option for UCC. This guideline presents suggestions for diagnosis and treatment based on the best available evidence and identifies gaps in our knowledge as well as areas for future research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".