Surgical Management of Craniovertebral Junction Schwannomas: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Craniovertebral junction (CVJ) schwannomas are rare, with surgery and stereotactic radiosurgery (SRS) being effective yet challenging options. We systematically reviewed the literature on CVJ schwannomas. METHODS: PubMed, Scopus, Web-of-Science, and Cochrane were searched following the PRISMA statement to include studies reporting CVJ schwannomas. Clinical features, management, and outcomes were analyzed. RESULTS: We collected 353 patients from 101 included articles. Presenting symptoms were mostly neck pain (30.3%) and headache (26.3%), with most cranial neuropathies involving the XII (31.2%) and X (24.4%) nerves. Most tumors originated from C2 (30.9%) and XII (29.4%) nerves, being extracranial (45.1%) and intradural-extradural (44.2%). Erosion of C1-C2 vertebrae (37.1%), the hypoglossal canal (28.3%), and/or jugular foramen (20.1%) were noted. All tumors were operated, preferably with the retrosigmoid approach (36.5%), with the far-lateral approach (29.7%) or with the posterior approach and cervical laminectomy (26.9%), far-lateral approaches (14.2%), or suboccipital craniotomy with concurrent cervical laminectomy (14.2%). Complete tumor resection was obtained most frequently (61.5%). Adjuvant post-surgery stereotactic radiosurgery was delivered in 5.9% patients. Median follow-up was 27 months (range, 12-252). Symptom improvement was noted in 88.1% of cases, and cranial neuropathies showed improvement in 10.2%. Post-surgical complications occurred in 83 patients (23.5%), mostly dysphagia (7.4%), new cranial neuropathies (6.2%), and cerebrospinal fluid leak (5.9%). A total of 16 patients (4.5%) had tumor recurrence and 7 died (2%), with median overall survival of 2.7 months (range, 0.1-252). CONCLUSIONS: Microsurgical resection is safe and effective for CVJ schwannomas. Data on SRS efficacy and indications are still lacking, and its role deserves further evaluation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".