A Novel Classification System for Spinal Instability in Neoplastic Disease
Bibliographic record
Abstract
In Brief Study Design. Systematic review and modified Delphi technique. Objective. To use an evidence-based medicine process using the best available literature and expert opinion consensus to develop a comprehensive classification system to diagnose neoplastic spinal instability. Summary of Background Data. Spinal instability is poorly defined in the literature and presently there is a lack of guidelines available to aid in defining the degree of spinal instability in the setting of neoplastic spinal disease. The concept of spinal instability remains important in the clinical decision-making process for patients with spine tumors. Methods. We have integrated the evidence provided by systematic reviews through a modified Delphi technique to generate a consensus of best evidence and expert opinion to develop a classification system to define neoplastic spinal instability. Results. A comprehensive classification system based on patient symptoms and radiographic criteria of the spine was developed to aid in predicting spine stability of neoplastic lesions. The classification system includes global spinal location of the tumor, type and presence of pain, bone lesion quality, spinal alignment, extent of vertebral body collapse, and posterolateral spinal element involvement. Qualitative scores were assigned based on relative importance of particular factors gleaned from the literature and refined by expert consensus. Conclusion. The Spine Instability Neoplastic Score is a comprehensive classification system with content validity that can guide clinicians in identifying when patients with neoplastic disease of the spine may benefit from surgical consultation. It can also aid surgeons in assessing the key components of spinal instability due to neoplasia and may become a prognostic tool for surgical decision-making when put in context with other key elements such as neurologic symptoms, extent of disease, prognosis, patient health factors, oncologic subtype, and radiosensitivity of the tumor. Spinal instability is poorly defined especially in neoplastic disease. The SOSG has developed a comprehensive classification system to define neoplastic spinal instability (the Spine Instability Neoplastic Score). The Spine Instability Neoplastic Score will aid oncologists and primary care physicians in determining timing of referral to spine surgeons and will aid surgeons in assessing need for surgical stabilization.
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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 | 0.134 | 0.222 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.035 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".