The Effect of Corticosteroids on Sinus Microbiota in Chronic Rhinosinusitis Patients with Nasal Polyposis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis with nasal polyposis (CRSwNP) is a multifactorial disease with no known single cause, but it is thought that bacteria play a role in the disease process. OBJECTIVE: This pilot study aims to assess the longitudinal effect of corticosteroid therapy on sinus microbiota in chronic rhinosinusitis patients with nasal polyposis (CRSwNP). METHODS: A longitudinal prospective case-control study was done on patients with CRSwNP and healthy controls. Patients with CRSwNP were randomly allocated to a corticosteroids and antibiotics treatment group (CRSwNP-SA) or a corticosteroid-only treatment group (CRSwNP-S). Data were collected at three-time points (before treatment, 1, and 3 months after treatment). Specimens were cultured and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) was used as a bacterial detection method. RESULTS: Data from 29 patients with CRSwNP (16 CRSwNP-SA and 13 CRSwNP-S) was compared to 15 healthy subjects. Patients reported significant symptom improvement initially (1 month), but not in the long-term (3 months). This result was found in both treatment groups, whether or not antibiotics were used. After 3 months from treatment, the prevalence of Corynebacterium genera tended to increase in the CRSwNP-SA, while Staphylococcus and Gram-negative genera (Pseudomonas) tended to increase in the CRSwNP-S. Smoking, aspirin sensitivity, and previous endoscopic sinus surgery were found to be co-factors significantly associated with the response to systemic corticosteroid therapy. CONCLUSION: In this pilot study, both treatment options were effective to improve symptoms in the short-term but not in the long-term, and were not linked to any clear sinus microbiota response. As a result, this study supports the avoidance of systemic antibiotics without evidence of active infection.
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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) | 0.000 | 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 | 0.000 |
| 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 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".