Multi‐center study on sellar reconstruction after endoscopic transsphenoidal pituitary surgery
Bibliographic record
Abstract
INTRODUCTION: Surgical techniques for sellar reconstruction include no reconstruction, use of synthetic materials, autologous grafts, and/or vascularized flaps. The aim of this study was to conduct a multi-center study comparing the efficacy and postoperative morbidity associated with different sellar reconstruction techniques. METHODS: A retrospective chart review of patients who underwent endoscopic transsphenoidal surgery for pituitary tumors from five participating sites between January 2021 and March 2023 was performed. The variables included demographics, tumor characteristics, reconstruction technique, postoperative cerebrospinal fluid leak (CSF) leak, and 22-item Sino-Nasal Outcome Test (SNOT-22) scores. Comparisons of postoperative complications, SNOT-22 scores, and duration of surgery by type of onlay reconstruction were evaluated using Fisher's exact test, analysis of variance, and Kruskal‒Wallis test. RESULTS: Five hundred and one patients were identified. The median tumor size was 2.1 cm, and 64% were non-functioning. Intraoperative CSF leak was identified in 38% of patients. A total of 89% of patients underwent onlay reconstruction: 49% were reconstructed with mucosal grafts, 35% with nasoseptal flaps, and 5% with other onlay techniques. Nasoseptal flaps were utilized more frequently in the setting of giant pituitary adenomas (>3 cm), medial cavernous sinus wall resection, and high-flow intraoperative CSF leaks. Cases who utilized mucosal grafts had an overall shorter operating time (median: 183 min vs. 240 min; p < 0.001). Five postoperative CSF leaks were identified, and therefore, statistical analysis could not be performed for this complication. CONCLUSION: The effectiveness and morbidity of different sellar reconstruction techniques are comparable. Vascularized flaps were utilized more frequently in the setting of larger tumors and high-flow intraoperative CSF leaks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".