Spinal Instability Neoplastic Score: An Analysis of Reliability and Validity From the Spine Oncology Study Group
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it