Risk Factors for Cerebrospinal Fluid Leakage After Extradural Spine Surgery: A Meta-Analysis and Systematic Review
Bibliographic record
Abstract
BACKGROUND: Cerebrospinal fluid (CSF) leakage is 1 of the common complications of spine surgery and is largely caused by intraoperative or postoperative dural tears. Associations of different factors with postoperative CSF leakage have not been consistent. In this study we aimed to identify demographic, disease-related, and surgical risk factors for CSF leakage after extradural spine surgery in a systematic review and meta-anlysis. METHODS: The PubMed, EMBASE, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, Chinese Wanfang data, Chinese Weipu Database, and SinoMed databases were searched from inception until October 24, 2022. Fixed-effects or random-effects models were used to calculate odds ratios and 95% confidence intervals. The quality of observational studies was evaluated using the Newcastle-Ottawa scale instrument. RESULTS: A total of 15 observational studies with 1,719,923 participants were included in this systematic review. All studies had a Newcastle-Ottawa scale score greater than or equal to 6. Age older than 70 years, smoking, ossification of the posterior longitudinal ligament, adhesion of spinal dura, spinal canal stenosis, cervical fracture, spondylolisthesis, revision surgery, and multiple surgical segments were all related to CSF leakage in the pooled analysis. Obesity and disease duration>1 year were not associated with the leakage of CSF. CONCLUSIONS: This study will provide a reference for the identification of patients at high risk of developing CSF leakage, which suggests clinicians to strengthen the observation of drainage fluid in high-risk groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".