Effect of Platelet-Rich Plasma Injection in Patients With Atrophic Rhinitis: A Systematic Review
Bibliographic record
Abstract
BackgroundAtrophic rhinitis (AR) is a chronic condition characterized by mucosal atrophy, crusting, and impaired mucociliary clearance. Current treatments are palliative and do not focus on tissue regeneration. Although platelet-rich plasma (PRP) has gained attention for its regenerative properties, evidence supporting its effectiveness in AR remains limited.ObjectiveTo systematically evaluate the effectiveness and safety of PRP in the treatment of AR, focusing on nasal symptom relief, mucociliary clearance, and tissue repair biomarkers.MethodsA systematic literature search was conducted in PubMed and the Cochrane Library from database inception to September 2024 for randomized clinical trials, prospective trials, and case series evaluating PRP for AR. Study quality was assessed using the Cochrane Risk of Bias tool and Newcastle-Ottawa Scale.ResultsPRP treatment resulted in significant improvements in nasal symptoms, with sino-nasal outcome test scores decreasing from 22.4 to 12.7. Four studies demonstrate enhanced mucociliary clearance, including a reduction in saccharin transit time from 420 s to 220 s in 1 study. Biochemical analyses revealed elevated levels of nitric oxide synthase and arginase, suggesting tissue regeneration. Improvements in anosmia and nasal obstruction symptom scores were also reported. No severe adverse effects were observed.ConclusionWhile PRP shows promise for AR, current evidence is compromised by diagnostic uncertainty, methodological inconsistencies, and potential commercial bias. Significant demographic variations suggest different patient populations were evaluated. Rigorous, independently funded trials with standardized protocols are essential before clinical recommendations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".