When Less Is More
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVE: The aim of this study was to review the techniques, indications, and outcomes of minimally invasive surgery (MIS) and separation surgery with subsequent radiosurgery in the treatment of patients with metastatic spine disease. SUMMARY OF BACKGROUND DATA: The utilization of MIS techniques in patients with spine metastases is a growing area within spinal oncology. Separation surgery represents a novel paradigm where radiosurgery provides long-term control after tumor is surgically separated from the neural elements. METHODS: PubMed, Embase, and CINAHL databases were systematically queried for literature reporting MIS techniques or separation surgery in patients with metastatic spine disease. PRISMA guidelines were followed. RESULTS: Of the initial 983 articles found, 29 met inclusion criteria. Twenty-five articles discussed MIS techniques and were grouped according to the primary objective: percutaneous stabilization (8), tubular retractors (4), mini-open approach (8), and thoracoscopy/endoscopy (5). The remaining 4 studies reported separation surgery. Indications were similar across all studies and included patients with instability, refractory pain, or neurologic compromise. Intraoperative variables, outcomes, and complications were similar in MIS studies compared to traditional approaches, and some MIS studies showed a statistically significant improvement in outcomes. Studies of mini-open techniques had the strongest evidence for superiority. CONCLUSIONS: Low-quality evidence currently exists for MIS techniques and separation surgery in the treatment of metastatic spine disease. Given the early promising results, the next iteration of research should include higher-quality studies with sufficient power, and will be able to provide higher-level evidence on the outcomes of MIS approaches and separation surgery. LEVEL OF EVIDENCE: N/A.
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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 | 0.050 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.002 |
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".