P.153 Predictors and clinical outcomes of postoperative cerebro spinal fluid leak after endoscopic endonasal skull base surgery
Bibliographic record
Abstract
Background: This study aimed to identify risk factors for postoperative cerebrospinal fluid (CSF) leaks and assess their outcomes following endoscopic endonasal approach (EEA) for resection of skull base tumors. Methods: A retrospective review was conducted of patients who underwent EEA for resection of intradural pathology between October 2001 and October 2023. Data on demographics, approach type, reconstruction technique, tumor pathology, complications and outcomes were analyzed. Results: A total of 542 patients were included, with 80.1% undergoing surgery for sellar or suprasellar pathology. Lumbar drains were used in 14.9%, and dural sealants in 57.7%. Forty patients (7.3%) developed postoperative CSF leaks, with the highest rate in sellar or suprasellar lesions (5.9%). CSF leaks were associated with longer hospital stays (p < 0.001), higher 30-day readmission rates (p < 0.001), increased sepsis risk (p = 0.021), and higher rates of diabetes insipidus (p < 0.001). Lumbar drains increased the incidence of CSF leaks (p = 0.021), while nasoseptal flap reconstruction reduced leak rates (p = 0.0015). Higher BMI and intraoperative CSF leaks were also significant risk factors (p = 0.001) Conclusions: CSF leaks are associated with increased complications and extended hospital stays, highlighting the need for vigilant intraoperative monitoring and targeted strategies.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".