Factors impacting cerebrospinal fluid leak rates in endoscopic sellar surgery
Bibliographic record
Abstract
BACKGROUND: In patients undergoing transnasal endoscopic sellar surgery, an analysis of risk factors and predictors of intraoperative and postoperative cerebrospinal fluid leak (CSF) would provide important prognostic information. METHODS: A retrospective review of patients undergoing endoscopic sellar surgery for pituitary adenomas or craniopharyngiomas between 2002 and 2014 at 7 international centers was performed. Demographic, comorbidity, and tumor characteristics were evaluated to determine the associations between intraoperative and postoperative CSF leaks. Correlations between reconstructive and CSF diversion techniques were associated with postoperative CSF leak rates. Odds ratios (OR) were identified using a multivariate logistic regression model. RESULTS: Data were collected on 1108 pituitary adenomas and 53 craniopharyngiomas. Overall, 30.1% of patients had an intraoperative leak and 5.9% had a postoperative leak. Preoperative factors associated with increased intraoperative leaks were mild liver disease, craniopharyngioma, and extension into the anterior cranial fossa. In patients with intraoperative CSF leaks, postoperative leaks occurred in 10.3%, with a higher postoperative leak rate in craniopharyngiomas (20.8% vs 5.1% in pituitary adenomas). Once an intraoperative leak occurred, craniopharyngioma (OR = 4.255, p = 0.010) and higher body mass index (BMI) predicted postoperative leak (OR = 1.055, p = 0.010). In patients with an intraoperative leak, the use of septal flaps reduced the occurrence of postoperative leak (OR = 0.431, p = 0.027). Rigid reconstruction and CSF diversion techniques did not impact postoperative leak rates. CONCLUSION: Intraoperative CSF leaks can occur during endoscopic sellar surgery, especially in larger tumors or craniopharyngiomas. Once an intraoperative leak occurs, risk factors for postoperative leaks include craniopharyngiomas and higher BMI. Use of septal flaps decreases this risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".