Does Cerebrospinal Fluid Leak in Pituitary Surgery Affect Patient Reported Quality of Life?
Bibliographic record
Abstract
BACKGROUND: Endoscopic techniques allow for improved visualization and tumor debulking of pituitary adenomas. More thorough tumor resection, however, can be associated with higher rates of CSF leaks. We set out to determine if CSF leaks influenced patient perceived quality of life outcomes. METHODS: This retrospective study included 152 patients who underwent endoscopic pituitary tumor resection over a 10-year period. QoL was assessed using the SF-36 questionnaire and completed before surgery, 6 weeks and 6 months post-operatively. Statistical analysis was conducted using a equivalence test and a two-way mixed model ANOVA to assess intraoperative CSF leak, postoperative CSF leak, redo surgery, and the use of a lumbar drain. RESULTS: Of the 152 patients, 98 had a potential intraoperative CSF leak. Intra- and postoperative CSF leaks did not significantly impact patient reported QoL outcomes at 6 months following surgery. There was clinical equivalence in mental scores as early as 6 weeks and 6 months for physical scores. There was no statistically significant difference in physical (p-value = 0.975) and mental (p = 0.204) scores for patients who experienced a postoperative CSF leak. There was no statistically significant difference in QoL in the mental and physical scores for patients that received a lumbar drain (physical score p = 0.832; mental score p = 0.915) or redo surgery (physical score p = 0.830; mental score p = 0.204). CONCLUSION: This article demonstrates that CSF leaks do not impact patient-reported QoL outcomes at 6 months post-surgery. This will allow surgeons to better provide insight and counsel patients regarding the relevance of CSF leaks in the setting of pituitary procedures. LEVEL OF EVIDENCE: 3 Laryngoscope, 135:1970-1974, 2025.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".