Surgical management of tumors of the cervical spine and craniovertebral junction involving the vertebral artery: A narrative review
Bibliographic record
Abstract
BACKGROUND: The vertebral artery (VA) is in close proximity to bony structures, nerves and nerve sheaths of the cervical spine and craniovertebral junction (CVJ). These structures can be sources of tumors that are responsible for displacement, encasement and sometimes invasion of the VA. Removing these tumors while minimizing the risk of vascular injury requires thorough knowledge of the vascular anatomy, risk factors of vascular injury, the relationships of each tumor type with the VA, and the different surgical approaches and techniques that result in the best outcomes in terms of vascular control, tumoral exposure and resection. OBJECTIVE: To present an overview of preoperative and anatomical considerations, differential diagnoses and various approaches to consider in cases of tumors in close relationship with the VA. METHOD: A review of recent literature was conducted to examine the anatomy of the VA, the tumors most frequently affecting it, surgical approaches, and the necessary pre-operative preparations for ensuring safe and maximal tumor resection. This review aims to underscore the principles of treatment. CONCLUSION: Tumors located at the CVJ and the cervical spine intimately involved with the VA, pose a surgical challenge and increase the risk of incomplete removal of the lesion. Detailed knowledge of the patient-specific anatomy and a targeted pre-operative work-up enable optimal planning of surgical approach and management of the VA, thereby reducing surgical risks and improving extent of resection.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".