Assessing the Risk of Irrigation Bottle and Fluid Contamination after Endoscopic Sinus Surgery
Bibliographic record
Abstract
BACKGROUND: Saline nasal irrigation has become an important aspect of post-operative care following endoscopic sinus surgery. The objective of this study was to identify the risks of contamination of both the nasal irrigation bottle and fluid following endoscopic sinus surgery. METHODS: This was a prospective study of consecutive patients undergoing endoscopic sinus surgery for chronic sinusitis. All patients were given nasal irrigation bottles with detailed cleaning instructions preoperatively. Nasal irrigation bottles were collected and cultured at 1 and 2 weeks postoperatively. During the same visit, 5-ml of sterile normal saline was mixed into the irrigation bottle and then cultured separately. RESULTS: A total of 20 patients agreed to participate in the study. At 1 week postoperatively, 50% of the bottles had positive cultures with 40% of the irrigation samples testing positive for bacteria. At two weeks, the contamination in the irrigation bottle and fluid decreased to 26.7% and 20%, respectively. The most common bacteria cultured was Pseudomonas aeruginosa. There were no cases of postoperative infection. CONCLUSION: Despite detailed cleaning instructions, there is a relatively high risk of bacterial contamination in nasal irrigation bottles and fluid following endoscopic sinus surgery. Although these risks did not translate into higher infection rates postsurgery, it may be important for physicians to emphasize regular cleaning techniques to minimize a potential source of bacterial contaminant exposure.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".