Comparison of Corticosteroids Effectiveness via High Volume Nasal Irrigation versus Conventional Nasal Spray after Endoscopic Sinus Surgery in Nasal Polyposi
Bibliographic record
Abstract
Objective: To compare the efficacy of two different methods of topical corticosteroid delivery techniques; conventional nasal spray versus high volume nasal irrigation with squeeze bottle, post-operatively in patients of nasal polyposis. Study Design: Quasi-experimental study. Place and Duration of Study: Department of Ear Nose Throat, Combined Military Hospital, Kohat Pakistan, from Sep 2021 to Aug 2022. Methodology: A total of 80 patients diagnosed with nasal polyposis were recruited for this study. Selected patients were divided into two equal groups. Pre-operatively all patients were carefully assessed with the help of Sino-nasal outcome test 22 (SNOT 22) and Lund Kennedy Endoscopy score for severity of symptoms. Post-operatively, Group-A received Betamethasone via high volume irrigation by squeeze bottle whereas Group-B received the Beclomethasone via conventional nasal spray. This treatment plan continued for 3 months. Post-operatively, SNOT 22 and Lund Kennedy Endoscopy score assessment was carried out at 3 months. Results: Both groups were comparable with respect to demographic data. In Group-A pre-op median and interquartile range for SNOT score was 68 and 10 respectively and it was reduced to 14 and 5 post-operatively, whereas in Group-B, it was 68 and 10; 27 and 5.5 pre and post-operatively. Post-operative difference was significant (p-value=0.001). Similarly, in Group-A, pre and post-operative median and interquartile range for LKE score 6 and 1 respectively and 2 and 0, whereas in Group-B it was 6.22 and 0.4 and 1.5 with statistically significant post-operative difference (p-value=0.001). Conclusion: High volume irrigation with squeeze bottle is more effective method of delivering....
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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) | 0.000 | 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".