Selective Irrigation of Paranasal Sinuses in the Treatment of Recalcitrant Chronic Sinusitis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) refractory to medical and surgical therapy is a challenging entity to treat. Topical antibiotics and anti-inflammatory drugs have been increasingly used in managing this disorder. The aim of this pilot study is to evaluate the role of maxillary sinus antrostomy tubes (MAST) in selectively irrigating paranasal sinuses with topical antibiotics with anti-inflammatory in treating recalcitrant CRS. A prospective clinical trial was performed at a tertiary referral center. METHODS: Thirteen patients with failed maximal medical and surgical therapies for chronic sinusitis were enrolled in the study. Endoscopic scores as well as the Sino-Nasal Outcome Test 16 (SNOT-16) scores were obtained before and 3, 8, and 16 weeks after maxillary sinus intubation with MAST. All patients received topical antibiotics with anti-inflammatory medication for 21 days. RESULTS: Statistically significant reductions in SNOT-16 and endoscopic scores were found before and after topical irrigations. Both scores continued to improve at the 8th and 16th weeks. CONCLUSION: Selective irrigation of maxillary sinuses using MAST is a viable alternative in treating recalcitrant CRS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".