Selective Irrigation of the Sinuses in the Management of Chronic Rhinosinusitis Refractory to Medical Therapy: A Promising Start
Bibliographic record
Abstract
BACKGROUND: Although endoscopic sinus surgery has been widely used for the treatment of chronic rhinosinusitis, some patients fail to derive clinical benefit from this procedure. We evaluated the efficacy of a treatment regimen consisting of selective irrigation of diseased sinus mucosa with topical antibiotics and steroids in conjunction with oral antibiotics and steroids. METHODS: Twenty patients suffering from chronic rhinosinusitis and resistant to medical treatment (mean duration 3.4 years) underwent intubations of the affected maxillary and/or ethmoid sinuses for irrigation for a duration of 21 to 30 days. A computed tomographic (CT) scan of the paranasal sinus was taken both pre- and post-treatment and staged according to the Lund-MacKay system. Clinical symptoms were scored for rhinorrhea, facial pain, nasal congestion, and smell at least 2 months prior to treatment and approximately 18 months after the follow-up. RESULTS: The clinical experience with the technique of intubation and irrigation was well tolerated by all patients. We found an improvement in all symptom scores, including rhinorrhea, nasal congestion, smell (n = 20; p < .001), and facial pain (n = 20; p < .01). Similar improvements were seen on the CT scans, with reduced staging from 14.6 +/- 1.1 to 5.6 +/- 1.1 (p < .001). Only three patients did not respond to selective irrigation of the sinuses and needed further surgery. CONCLUSION: These results suggest that sinus irrigation could provide a reasonable and effective alternative to ethmoidectomy with drainage procedures and offer promise for the treatment of patients with chronic rhinosinusitis who are resistant to medical treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".