Management of Persistent Cerebrospinal Fluid Leakage Following Thoraco-lumbar Surgery
Bibliographic record
Abstract
STUDY DESIGN: This was a retrospective study of patients who had developed a dural tear after thoracic and lumbar spine surgery that was not recognized during the surgery, and was treated either by lumbar drainage or over-sewing of the wounds. PURPOSE: To revisit the treatment strategies in postoperative dural leaks and present our experience with over-sewing of the wound and lumbar drainage. OVERVIEW OF LITERATURE: Unintended durotomy is a frequent complication of spinal surgery. Management of subsequent cerebrospinal fluid leakage remains controversial. There is no distinct treatment guideline according to the etiology in the current literature. METHODS: The records of 368 consecutive patients who underwent thoracic and/or lumbar spine surgery from 2006 throug h 2010 were retrospectively reviewed. Seven cerebrospinal fluid fistulas and five pseudomeningoceles were noted in 12 (3.2%) procedures. Cerebrospinal fluid diversion by lumbar drainage in five pseudomeningoceles and over-sewing of wounds in seven cerebrospinal fluid fistulas employed in 12 patients. Clinical grading was evaluated by Wang. RESULTS: Of the 12 patients who had a dural tear, 5 were managed successfully with lumbar drainage, and 7 with oversewing of the wound. The clinical outcomes were excellent in 9 patients, good in 2, and poor in 1. Complications such as neurological deficits, or superficial or deep wound infections did not develop. A recurrence of the fistula or pseudomeningocele after the treatment was not seen in any of our patients. CONCLUSIONS: Pseudomeningoceles respond well to lumbar drainage, whereas over-sewing of the wound is an alternative treatment option in cerebrospinal fluid fistulas without neurological compromise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".