Paraspinous Muscle Flaps for the Treatment and Prevention of Cerebrospinal Fluid Fistulas in Neurosurgery
Bibliographic record
Abstract
STUDY DESIGN: A prospective clinical study was conducted to evaluate the efficacy of paraspinous muscle flaps in preventing and managing cerebrospinal fluid fistulas in high-risk neurosurgery patients. OBJECTIVES: To evaluate the efficacy of paraspinous muscle flap coverage using a "vest-over-pants" closure in the prevention and treatment of cerebrospinal fluid fistulas. SUMMARY OF BACKGROUND DATA: Previous studies have described paraspinous muscle flaps for the closure of complex spinal wounds, but none has addressed their use for the prevention and treatment of cerebrospinal fluid fistulas. METHODS: This prospective clinical study evaluated nine consecutive patients with either refractory cerebrospinal fluid fistulas or high risk for cerebrospinal fluid leaks after spinal surgery. Bilateral paraspinous muscle flaps were used as primary flaps and closed using an overlapping vest-over-pants technique in eight of nine cases. The latissimus dorsi and trapezius muscles were recruited as additional muscle flaps for closure of thoracolumbar and high thoracic deficits, respectively. RESULTS: Paraspinous muscle flaps provided immediate wound coverage in seven high-risk patients undergoing spinal surgery and two patients with recurrent cerebrospinal fluid fistulas. Postoperative hospitalization averaged 14.4 days. There was no evidence of a cerebrospinal fluid fistula after an average follow-up of 176.7 days. No wound infections occurred. The only complications were a superficial hematoma, which was drained percutaneously on postoperative day 6, and a seroma, which was drained during the follow-up period and eventually resolved. CONCLUSIONS: Paraspinous muscle flaps allow effective treatment and prevention of cerebrospinal fluid fistulas in selected high-risk patients and provide simple durable coverage of complex spinal wounds with minimal morbidity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".