The Application of a Free Nasal Floor Mucoperiosteal Graft in Endoscopic Sinus Surgery
Bibliographic record
Abstract
BACKGROUND: Numerous reconstructive techniques and materials have been reported for repair of skull base defects, cerebrospinal fluid (CSF) leaks, and coverage of denuded bone, including pedicled vascularized flaps and free mucosal grafts. OBJECTIVE: This study described our technique of harvesting and transferring a free nasal floor mucoperiosteal graft and discussed our experience with the application of this technique. METHODS: A retrospective review of 19 patients (mean age, 53.7 years; 13 men, 6 women) treated with image-guided endoscopic sinus surgery for chronic rhinosinusitis or tumors. Intraoperative free mucosal graft repair was performed for large skull base defects after resection of skull base tumor (n = 7), CSF leak (n = 12), and iatrogenic CSF leak (n = 7). Repair was performed in an overlay or an underlay fashion, with a multilayer approach in cases of a large skull base defect. Patients underwent endoscopic assessment at 6 days, 5 weeks, and 12 weeks after surgery for assessment of healing and of CSF leak. The patients were followed up for a mean of 8.7 months. RESULTS: Minimal crusting was identified at the donor site in all the patients at 6 days, with no evidence of CSF leak. In cases of exposed bone and/or mucosal stripping, hyperostosis at the recipient graft site was avoided. All the patients had complete healing at the donor site and the recipient site, with minimal morbidity at 5 and 12 weeks, and no evidence of recurrent CSF leak. CONCLUSION: The use of nasal floor mucoperiosteal free grafts in endoscopic surgery offered the advantage of ease of harvest, coverage of large defects, and multiple applications of use, with minimal donor-site morbidity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".