Biofilm‐forming bacteria and quality of life improvement after sinus surgery
Bibliographic record
Abstract
BACKGROUND: It remains unclear how much chronic rhinosinusitis (CRS) patients with bacterial biofilms can benefit from functional endoscopic sinus surgery (FESS). We aimed to evaluate whether biofilm-forming bacteria was associated with quality of life (QOL) improvement after FESS. METHODS: This retrospective cohort study included adult CRS patients who underwent FESS from 2008 to 2011. Sinus samples were taken to evaluate for biofilm-formation in vitro using a modified Calgary Biofilm Detection Assay. QOL was measured before FESS, and 1-month, 3-month, and 6-month after FESS using 22-item Sino-Nasal Outcome Test (SNOT-22) scores. Patients' characteristics and medications were collected. Clinical significant QOL change was defined as a difference of at least 0.5 standard deviation (SD) of baseline SNOT-22 score in the reference group. RESULTS: A total of 156 patients had complete data, and 15% had biofilm-forming bacteria (n = 24). Patients with biofilm-forming bacteria had significantly worse preoperative SNOT-22 scores compared to patients without biofilm-forming bacteria (48 ± 20 vs 38 ± 23, p = 0.048). Both groups had clinically significant QOL improvement after FESS, and the differences in their 1-month (23 ± 19 vs 17 ± 20) and 3-month (27 ± 18 vs 18 ± 19) post-FESS SNOT-22 scores were not significant. However, patients with biofilm-forming bacteria demonstrated significantly less QOL improvement than patients without biofilm-forming bacteria from pre-FESS to 6-month post-FESS visits after adjusting for clinical factors (35 ± 25 vs 14 ± 15; β-coefficient = 0.71; 95% confidence interval [CI], 0.13 to 1.28; p = 0.016). CONCLUSION: CRS patients with biofilm-forming bacteria demonstrated clinically significant QOL improvement following FESS, but the degree of improvement was decreased overtime and became significantly worse than patients without biofilm-forming bacteria by 6-month follow-up. This QOL worsening was independent of other risk factors for CRS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".