Spinal Instability Neoplastic Score: An Analysis of Reliability and Validity From the Spine Oncology Study Group
Bibliographic record
Abstract
PURPOSE: Standardized indications for treatment of tumor-related spinal instability are hampered by the lack of a valid and reliable classification system. The objective of this study was to determine the interobserver reliability, intraobserver reliability, and predictive validity of the Spinal Instability Neoplastic Score (SINS). METHODS: Clinical and radiographic data from 30 patients with spinal tumors were classified as stable, potentially unstable, and unstable by members of the Spine Oncology Study Group. The median category for each patient case (consensus opinion) was used as the gold standard for predictive validity testing. On two occasions at least 6 weeks apart, each rater also scored each patient using SINS. Each total score was converted into a three-category data field, with 0 to 6 as stable, 7 to 12 as potentially unstable, and 13 to 18 as unstable. RESULTS: The κ statistics for interobserver reliability were 0.790, 0.841, 0.244, 0.456, 0.462, and 0.492 for the fields of location, pain, bone quality, alignment, vertebral body collapse, and posterolateral involvement, respectively. The κ statistics for intraobserver reliability were 0.806, 0.859, 0.528, 0.614, 0.590, and 0.662 for the same respective fields. Intraclass correlation coefficients for inter- and intraobserver reliability of total SINS score were 0.846 (95% CI, 0.773 to 0.911) and 0.886 (95% CI, 0.868 to 0.902), respectively. The κ statistic for predictive validity was 0.712 (95% CI, 0.676 to 0.766). CONCLUSION: SINS demonstrated near-perfect inter- and intraobserver reliability in determining three clinically relevant categories of stability. The sensitivity and specificity of SINS for potentially unstable or unstable lesions were 95.7% and 79.5%, respectively.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".