Reliability of the Spinal Instability Neoplastic Score (SINS) among radiation oncologists: an assessment of instability secondary to spinal metastases
Bibliographic record
Abstract
BACKGROUND: The Spinal Instability Neoplastic Score (SINS) categorizes tumor related spinal instability. It has the potential to streamline the referral of patients with established or potential spinal instability to a spine surgeon. This study aims to define the inter- and intra-observer reliability and validity of SINS among radiation oncologists. METHODS: Thirty-three radiation oncologists, across ten international sites, rated 30 neoplastic spinal disease cases. For each case, the total SINS (0-18 points), three clinical categories (stable: 0-6 points, potentially unstable: 7-12 points, and unstable: 13-18 points), and a binary scale ('stable': 0-6 points and 'current or possible instability'; surgical consultation recommended: 7-18 points) were recorded. Evaluation was repeated 6-8 weeks later. Inter-observer agreement and intra-observer reproducibility were calculated by means of the kappa statistic and translated into levels of agreement (slight, fair, moderate, substantial, and excellent). Validity was determined by comparing the ratings against a spinal surgeon's consensus standard. RESULTS: Radiation oncologists demonstrated substantial (κ=0.76) inter-observer and excellent (κ=0.80) intra-observer reliability when using the SINS binary scale ('stable' versus 'current or possible instability'). Validity of the binary scale was also excellent (κ=0.85) compared with the gold standard. None of the unstable cases was rated as stable by the radiation oncologists ensuring all were appropriately recommended for surgical consultation. CONCLUSIONS: Among radiation oncologists SINS is a highly reliable, reproducible, and valid assessment tool to address a key question in tumor related spinal disease: Is the spine 'stable' or is there 'current or possible instability' that warrants surgical assessment?
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".