Minimally invasive surgery for spinal cerebrospinal fluid–venous fistula ligation: patient series
Bibliographic record
Abstract
BACKGROUND: Cerebrospinal fluid-venous fistulas (CVFs) may cause cerebrospinal fluid leaks resulting in spontaneous intracranial hypotension (SIH). Surgical treatment of CVFs aims to eliminate abnormal fistulous connections between the subarachnoid space and the epidural venous plexus at the level of the nerve root sleeve. The authors propose a percutaneous minimally invasive technique for surgical ligation of CVF as an alternative to the traditional open approach using a tubular retractor system. OBSERVATIONS: Minimally invasive surgical (MIS) ligation of spinal CVF was performed in 5 patients for 6 CVFs. The definite disconnection of the CVF was achieved in all patients by clipping and additional silk tie ligation of the fistula. None of the patients experienced surgical complications or required transition to an open procedure. One patient underwent 2 MIS procedures for 2 separate CVFs. Postoperative clinical follow-up and cranial magnetic resonance imaging confirmed resolution of symptoms and radiographic SIH stigmata. LESSONS: MIS ligation of CVFs is safe and efficient. It represents an elegant and less invasive procedure, reducing the risk of wound infections and time to recovery. However, preparedness for open ligation is warranted within the same surgical setting in cases of complications and difficult accessibility.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".