From Research to Clinical Practice: Long-Term Impact of Randomized Clinical Trial Examining the Effect of Lumbar Drains on Cerebrospinal Fluid Leak Rates Following Endonasal Skull Base Surgery
Bibliographic record
Abstract
Abstract Intraoperative cerebrospinal fluid (CSF) leaks are associated with increased risk of postoperative CSF leaks despite multilayered reconstruction with vascularized tissue. A recent randomized controlled trial (RCT) examining the use of perioperative lumbar drains (LD) in high-risk skull base defects identified a significant reduction in postoperative CSF leak incidence (21.2 vs. 8.2%; p = 0.017). This study was conducted to assess the efficacy of the selective use of CSF diversion, for patients with intraoperative CSF leaks involving endoscopic endonasal approaches (EEA) to the skull base. Method Consecutive endoscopic endonasal surgeries of the skull base from a pre-RCT cohort and post-RCT cohort were compared. The following case characteristics between the two cohorts were examined: patient age, body mass index (BMI), rate of revision surgery, tumor histology, use of CSF diversion, and vascularized reconstruction. The primary measured outcome was postoperative CSF leak. Results The pre-RCT cohort included 76 patients and the post-RCT cohort included 77 patients, with dural defects in either the anterior or posterior cranial fossa (pituitary and parasellar/suprasellar surgeries excluded). There was a significant reduction in the incidence of postoperative CSF leak in the post-RCT cohort (27.6 vs. 12.9%; p = 0.04). On subgroup analysis, there was a trend toward improvement in CSF leak rate of the anterior cranial fossa (19.2 vs. 10.5%; p = 0.27), whereas CSF leak rates of the posterior cranial fossa were significantly reduced compared with the pre-RCT cohort (41.4 vs. 12.8%; p = 0.02). Conclusion This study demonstrates that the integration of selective CSF diversion into the reconstructive algorithm improved postoperative CSF leak rates.
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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 | 0.046 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".