Georg schmorl prize of the German spine society (DWG) 2021: Spinal Instability Spondylodiscitis Score (SISS)—a novel classification system for spinal instability in spontaneous spondylodiscitis
Bibliographic record
Abstract
PURPOSE: Even though spinal infections are associated with high mortality and morbidity, their therapy remains challenging due to a lack of established classification systems and widely accepted guidelines for surgical treatment. This study's aim therefore was to propose a comprehensive classification system for spinal instability based on the Spinal Instability Neoplastic Score (SINS) aiding spine surgeons in choosing optimal treatment for spontaneous spondylodiscitis. METHODS: Patients who were treated for spontaneous spondylodiscitis and received computed tomography (CT) imaging were included retrospectively. The Spinal Instability Spondylodiscitis Score (SISS) was developed by expert consensus. SINS and SISS were scored in CT-images by four readers. Intraclass correlation coefficients (ICCs) and Fleiss' Kappa were calculated to determine interrater reliabilities. Predictive validity was analyzed by cross-tabulation analysis. RESULTS: A total of 127 patients were included, 94 (74.0%) of which were treated surgically. Mean SINS was 8.3 ± 3.2, mean SISS 8.1 ± 2.4. ICCs were 0.961 (95%-CI: 0.949-0.971) for total SINS and 0.960 (95%-CI: 0.946-0.970) for total SISS. SINS yielded false positive and negative rates of 12.5% and 67.6%, SISS of 15.2% and 40.0%, respectively. CONCLUSION: We show high reliability and validity of the newly developed SISS in detecting unstable spinal lesions in spontaneous spondylodiscitis. Therefore, we recommend its use in evaluating treatment choices based on spinal biomechanics. It is, however, important to note that stability is merely one of multiple components in making surgical treatment decisions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".