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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".