P.122 Foramen magnum decompression of Chiari malformation using minimally invasive tubular retractors
Bibliographic record
Abstract
Background: A surgical technique for foramen magnum decompression of Chiari malformation I in 11 patients is described. Methods: We used minimally invasive tubular retractors (METRx Quadrant) attached to a flexible arm to keep the retractor in a fixed position, while allowing flexible angulation under fluoroscopic guidance. Despite the small surgical opening, this approach allowed access to a wide working area, minimized soft tissue exposure, and optimized extent of decompression. For some patients, only the outer layer of dura was opened, but in cases where clinically indicated, a duraplasty was performed. Results: Postoperative CT head demonstrated satisfactory bony removal, and MRI with CSF flow study showed restoration, or significant improvement to CSF flow around the foramen magnum. There was a low incidence of post-operative complications, and the average length of hospital stay was around 1 day (1.2). For 10 out of 11 patients; their symptoms completely resolved on last follow-up, and for those who had syringomyelia, they demonstrated a radiological evidence of syrinx reduction or resolution. Conclusions: Based on our experience with this technique in foramen magnum decompression of Chiari malformation I, minimally invasive tubular retractor is a useful tool, providing the surgeon with enhanced visualization of the operative field, while reducing potential damage to tissue, and optimizing surgical outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".