P.207 Endoscopic odontoidectomy for anterior cervicomedullary junction decompression
Bibliographic record
Abstract
Background: Endonasal endoscopic odontoidectomy (EEO) is a well-established method for treating symptomatic ventral compression at the cranio-cervical junction (CCJ). This study aims to review the clinical outcomes of patients undergoing EEO, focusing on clinical presentation, progression, and prognostic factors. Methods: We retrospectively analyzed data from patients who underwent EEO between October 2001 and October 2023. Information was collected on demographics, indications, reconstruction techniques, complications, fusion requirements, readmission rates, and outcomes. Results: Fifteen patients were included, with 60% classified as ASA class III. The majority presented with myelopathy (80%). Indications for surgery included basilar invagination, Chiari malformation, and rheumatoid arthritis. The mean blood loss was 317 ml. No perioperative lumbar drains were used, and 26.7% of patients had intraoperative CSF leaks, though no postoperative leaks were noted. A pedicled nasal flap was required in 66.7% of cases. Fourteen patients needed occipitocervical fusion, and six were readmitted within 30 days due to bulbar deficits. At the last follow-up, 86.6% of patients experienced symptom improvement. A significant association was found between decompression extent and symptomatic improvement (p=0.003). Conclusions: EEO is a safe and effective method for CCJ decompression, often accompanied by posterior cervical stabilization, with most patients showing symptomatic improvement and a low complication rate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".