Classification of Symptomatic Chiari I Malformation to Guide Surgical Strategy
Bibliographic record
Abstract
BACKGROUND: Treatment options for Chiari I malformations include posterior fossa decompression (PFD) with additional techniques including laminectomy, intradural exploration, and duraplasty. Neuroimaging findings of cisterna magna volume, syringomyelia, and intraoperative ultrasonography may tailor surgical intervention. METHODS: We developed an algorithm classifying symptomatic Chiari I patients into three groups to define minimum operation. Without syringomyelia, the presence of cisterna magna defined Group A and the absence defined Group B. Patients with syrinx formed Group C. Mild structural pathology (Group A) or adequate space following PFD (Group B, normal intraoperative ultrasound (IOUS)) should be treated by PFD alone. Conversely, presence of syringomyelia (Group C) or inadequate space following PFD (Group B, abnormal IOUS) should additionally have duraplasty. We applied this algorithm to patients treated at a single institution over 16 years. RESULTS: Twenty-four symptomatic Chiari I malformation patients were divided into three groups that did not differ by age, gender, or extent of tonsillar ectopia. All patients treated by this algorithm experienced clinical and radiographic improvement. This included eight Group B patients who underwent PFD only (n=6) or additional duraplasty (n=2) decided by IOUS. CONCLUSION: Treatment of symptomatic Chiari I malformation may have inadequate outcome with conservative strategy or complications with aggressive strategy. This algorithm utilizes preoperative neuroimaging and intraoperative ultrasound to tailor intervention, with excellent clinical outcome and radiographic syrinx resolution on application to 24 patients. Further validation requires prospective multicenter evaluation with larger patient population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".