Diagnostic Approach, Treatment, and Outcomes of Cervical Sympathetic Chain Schwannomas: A Global Narrative Review
Bibliographic record
Abstract
OBJECTIVE: This review examined the diagnostic approach, surgical treatment, and outcomes of cervical sympathetic chain schwannomas (CSCS) to guide clinical decision making. DATA SOURCES: Medline, EMBASE, and Cochrane databases. REVIEW METHODS: A literature review from 1998 to 2013 identified 156 articles of which 51 representing 89 CSCS cases were evaluated in detail. Demographic, clinical, and outcomes data were extracted by 2 independent reviewers with high interrater reliability (κ = .79). Cases were mostly international (82%), predominantly from Asia (50%) and Europe (27%). CONCLUSIONS: On average, patients were 42.6 years old (SD = 13.3) and had a neck mass ranging between 2 to 4 cm (52.7%) or >4 cm (43.2%). Nearly 70% of cases were asymptomatic at presentation. Presurgical diagnosis relied on CT (63.4%), MRI (59.8%), or both (19.5%), supplemented by cytology (33.7%), which was nearly always inconclusive (96.7%). US-treated cases were significantly more likely to receive presurgical MRI than internationally treated cases but less likely to have cytology (P < .05). Presurgical diagnosis was challenging, with only 11% confirmatory accuracy postsurgically. Irrespective of mass size, extracapsular resection (ie, complete resection with nerve sacrifice) was the most frequently (87.6%) performed surgical procedure. Common postsurgical adverse events included Horner's syndrome (91.1%), first bite syndrome (21.1%), or both (15.7%), with higher prevalence when mass size was >4 cm. Adverse events persisted in 82.3% of cases at an average 30.0 months (SD = 30.1) follow-up time. IMPLICATIONS FOR PRACTICE: Given the typical CSCS patient is young and asymptomatic and the likelihood of persistent morbidity is high with standard surgical approaches, less invasive treatment options warrant consideration.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".