Herbal Medicines for the Treatment of Rhinosinusitis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To assess the efficacy of herbal medicines for treating rhinosinusitis. DATA SOURCE: Five electronic databases, bibliographies of located papers, manufacturers, and experts in the field. REVIEW METHODS: Inclusion of randomized clinical trials (RCT) testing any herbal medicine in rhinosinusitis, as sole or adjunctive treatment. Data were extracted independently by two reviewers following a predetermined protocol. RESULTS: Ten RCTs, testing six different herbal products against placebo (8 RCTs) or "no additional treatment" (2 RCTs) were included. Four RCTs tested Sinupret as adjunctive treatment for either acute (3 RCTs) or chronic (1 RCT) rhinosinusitis. The quality of these studies varied, but two in acute sinusitis, including the largest and best quality study, and one in chronic sinusitis reported significant positive findings. Three RCTs tested bromelain in either acute sinusitis (2 RCTs) or patients of mixed diagnosis (chronic and acute sinusitis), and all reported some positive findings. Metanalysis of the two RCTs in acute sinusitis suggested that adjunctive use of bromelain significantly improves some symptoms of acute rhinosinusitis. Single RCTs were identified for 4 other herbal products (Esberitox, Myrtol, Cineole, and Bi Yuan Shu) as treatments for sinusitis, all reported some positive results. The median methodological quality score was 3 of 5. CONCLUSION: Evidence that any herbal medicines are beneficial in the treatment of rhinosinusitis is limited, particularly in chronic rhinosinusitis. There is encouraging evidence that Sinupret and bromelain may be effective adjunctive treatments in acute rhinosinusitis. Positive results from isolated RCTs of four other herbal products require independent replication.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".