Effect of oxidized cellulose on human respiratory mucosa and submucosa and its implications for endoscopic skull‐base approaches
Bibliographic record
Abstract
BACKGROUND: Regenerated oxidized cellulose (ROC) sheets have gained popularity as an adjunct to a vascularized nasoseptal flap for closure of dural defects after endoscopic endonasal skull-base approaches (EESBS). However, evidence supporting its impact on the healing process is uncertain. This study was performed to evaluate the impact of ROC on the nasal mucosa and assess its effects on tissue pH, structure, and cell viability. METHODS: piece of ROC gauze was placed on the surface of the middle turbinate before it was resected as part of a standard EESBS. Mucosa treated with ROC was separated from untreated mucosa and a histologic examination of structural changes in the respiratory epithelium was performed. To assess the effect of ROC on pH, increasing amounts of ROC were added to culture medium. Nasal fibroblasts viability was assessed in the presence of ROC before and after the pH was neutralized. RESULTS: Compared with unexposed controls, treated mucosa exhibited a higher incidence of cell necrosis and epithelial cell detachment. When added to Dulbecco's modified Eagle medium, ROC caused a dose-dependent decrease in pH of the medium. Only 1 ± 0.8% of cultured fibroblasts exposed to the ROC-induced acidic medium were alive, whereas 98.25 ± 0.5% of the cells were viable when the pH was neutralized (p < 0.001). CONCLUSION: ROC applied in vivo to nasal mucosa induced epithelial necrosis likely by diminishing the medium pH, because pH neutralization prevents its effect. The ultimate effect of this material on the healing process is yet to be determined.
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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 | 0.000 |
| 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) | 0.001 | 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".