Washing Illness Away: A Systematic Review of the Impact of Nasal Irrigation and Spray on <scp>COVID</scp>‐19
Bibliographic record
Abstract
OBJECTIVE: Nasal irrigation is a common treatment for sinonasal disorders; however, it is unknown if it can reduce SARS-CoV-2 nasopharyngeal viral load (NVL). This systematic review investigated the efficacy of nasal irrigation with saline, povidone iodine (PVP-I), and intranasal corticosteroids (INCS) at reducing SARS-CoV-2 NVL and transmissibility. DATA SOURCES: Databases including Embase, MEDLINE, Web of Science, and ClinicalTrials.gov. REVIEW METHODS: A systematic review was completed with pre-defined search criteria using keywords related to nasal irrigation and COVID-19 from 1946 through January 2024. This review followed PRISMA reporting guidelines and was registered on PROSPERO. Only in-vivo studies testing nasal irrigation with either saline, PVP-I, or INCS for reducing NVL were included. RESULTS: Nine out of ten studies on saline-based solutions reported positive effects in reducing NVL, with benefits noted in earlier time to negative nasopharyngeal PCR and a greater decline in NVL during early study time points, compared with controls. Isotonic and hypertonic saline mediums were found to be effective with three studies demonstrating enhanced efficacy with additives. Four out of seven studies on PVP-I showed a positive effect on reducing NVL, but results were heterogenous. Four studies demonstrated reduction of transmission with saline or PVP-I. No studies were found on INCS. CONCLUSION: Saline nasal irrigation showed the best efficacy in reducing SARS-CoV-2 NVL. Additives to saline may have a clinical benefit, but further studies are needed to elucidate their isolated impacts on NVL. Data on PVP-I is inconclusive and further studies are warranted to determine the ideal concentration for irrigation. Laryngoscope, 135:517-528, 2025.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".