Risk factors for postoperative CSF leakage after endonasal endoscopic skull base surgery: a meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: Cerebrospinal fluid (CSF) leakage is a complication that any surgeon working in the field of skull base surgery does not wish to encounter. The surgical approach to the skull base often varies, and the various sizes and locations of skull base lesions make it difficult to determine the cause of CSF leakage. However, it is useful to investigate which factors contribute to postopera- tive CSF leakage. METHODS: Related studies were identified by searching the following databases: PubMed/Medline, Embase, and Web of Sciences through December 2019. Random-effects models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs). The Newcastle-Ottawa scale was used to evaluate the quality of observational studies. RESULTS: Our search yielded 56 retrospective cohort studies involving a total of 11,826 skull base surgical procedures. The overall rate of postoperative CSF leakage was 7.2%. The effect of obesity on postoperative CSF leakage had an OR of 1.88, and the effect of perioperative radiotherapy on postoperative CSF leakage yielded an OR of 1.87. High intraoperative CSF flow rate also had a significant OR of 2.98. On the other hand, a pedicled vascularized flap efficiently reduced the risk of postoperative CSF leakage. Defect size and the presence or absence of a lumbar drain had no effect on postoperative CSF leakage. CONCLUSIONS: This comprehensive quantitative assessment of postoperative CSF leakage showed that obesity, perioperative radiotherapy, and high intraoperative CSF flow rate raised the risk of CSF leakage; however, a pedicled vascularized flap can ef- fectively reduce the risk of postoperative CSF leakage.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".