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
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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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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