Impact of the Spinal Instability Neoplastic Score on Surgical Referral Patterns and Outcomes
Bibliographic record
Abstract
BACKGROUND: The Spinal Instability Neoplastic Score (sins) was developed to identify patients with spinal metastases who may benefit from surgical consultation. We aimed to assess the distribution of sins in a population-based cohort of patients undergoing palliative spine radiotherapy (rt) and referral rates to spinal surgery pre-rt. Secondary outcomes included referral to a spine surgeon post-rt, overall survival, maintenance of ambulation, need for re-intervention, and presence of spinal adverse events. METHODS: We retrospectively reviewed ct simulation scans and charts of consecutive patients receiving palliative spine rt between 2012 and 2013. Data were analyzed using Student's t-test, Chi-squared, Fisher's exact, and Kaplan-Meier log-rank tests. Patients were stratified into low (<7) and high (≥7) sins groups. RESULTS: We included 195 patients with a follow-up of 6.1 months. The median sins was 7. The score was 0 to 6 (low, no referral recommended), 7 to 12 (intermediate, consider referral), and 13 to 18 (high, referral suggested) in 34%, 59%, and 7% of patients, respectively. Eleven patients had pre-rt referral to spine surgery, with a surgery performed in 0 of 1 patient with sins 0 to 6, 1 of 7 with sins 7 to 12, and 1 of 3 with sins 13 to 18. Seven patients were referred to a surgeon post-rt with salvage surgery performed in two of those patients. Primary and secondary outcomes did not differ between low and high sins groups. CONCLUSION: Higher sins was associated with pre-rt referral to a spine surgeon, but most patients with high sins were not referred. Higher sins was not associated with shorter survival or worse outcome following rt.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".