Spinal arachnoid cysts: A case series & systematic review of the literature
Bibliographic record
Abstract
Introduction: Spinal arachnoid cysts (SACs) are rare lesions with challenging and controversial management. Research question: We analyzed our experiences from a case series and provide a systematic review to determine 1) Demographic and clinical features of SACs, 2) Optimal imaging for diagnosis and operative planning, 3) Optimal management of SACs, and 4) Clinical outcomes following surgery. Materials and methods: A single-institution, ambispective analysis of patients with symptomatic SACs surgically managed between May 2005 and May 2019 was performed. Data were collected as per local ethics committee stipulations. A systematic review of SACs in adults was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and a preapproved protocol. Results: Our series consisted of 11 patients, M:F 8:3, mean age 47.8 years (range 18-73 years). Mean follow-up was 19 months (range 5-36 months). SACs were excised or marsupialised (7), fenestrated (3) or partially excised (1). Eight patients had expansile duroplasty, 3 primary dural closure. One patient had a cystoperitoneal shunt. All patients were AIS D preoperatively; 4 remained unchanged and 7 improved to AIS E at follow-up. Our systematic search retrieved 725 citations. Fourteen case series met the inclusion criteria. There was no evidence to support superiority of one surgical strategy over another. Surgery for symptomatic patients resulted in positive clinical outcomes. Discussion and conclusions: Symptomatic SACs require surgical intervention. Limited evidence suggests that decompressing the cord, breakdown of arachnoid adhesions, and establishing CSF flow by consideration of expansile duroplasty are important for positive outcomes.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".