The Spinal Instability Neoplastic Score
Bibliographic record
Abstract
STUDY DESIGN: Systematic literature review. OBJECTIVE: To address the following questions in a systematic literature review: 1. How is spinal neoplastic instability defined or classified in the literature before and after the introduction of the Spinal Instability Neoplastic Score (SINS)? 2. How has SINS affected daily clinical practice? 3. Can SINS be used as a prognostic tool? SUMMARY OF BACKGROUND DATA: Spinal neoplastic-related instability was defined in 2010 and simultaneously SINS was introduced as a novel tool with criteria agreed upon by expert consensus to assess the degree of spinal stability. METHODS: PubMed, Embase, and clinical trial databases were searched with the key words "spinal neoplasm," "spinal instability," "spinal instability neoplastic score," and synonyms. Studies describing spinal neoplastic-related instability were eligible for inclusion. Primary outcomes included studies describing and/or defining neoplastic-related instability, SINS, and studies using SINS as a prognostic factor. RESULTS: The search identified 1414 articles, of which 51 met the inclusion criteria. No precise definition or validated assessment tool was used specific to spinal neoplastic-related instability prior to the introduction of SINS. Since the publication of SINS in 2010, the vast majority of the literature regarding spinal instability has used SINS to assess or describe instability. Twelve studies specifically investigated the prognostic value of SINS in patients who underwent radiotherapy or surgery. CONCLUSION: No consensus could be determined regarding the definition, assessment, or reporting of neoplastic-related instability before introduction of SINS. Defining spinal neoplastic-related instability and the introduction of SINS have led to improved uniform reporting within the spinal neoplastic literature. Currently, the prognostic value of SINS is controversial. LEVEL OF EVIDENCE: N/A.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".