Subaxial cervical spine trauma classification: the Subaxial Injury Classification system and case examples
Bibliographic record
Abstract
Object The authors review a novel subaxial cervical trauma classification system and demonstrate its application through a series of cervical trauma cases. Methods The Spine Trauma Study Group collaborated to create the Subaxial Injury Classification (SLIC) and Severity score. The SLIC system is reviewed and is applied to 3 cases of subaxial cervical trauma. Results The SLIC system identifies 3 major injury characteristics to describe subaxial cervical injuries: injury morphology, discoligamentous complex integrity, and neurological status. Minor injury characteristics include injury level and osseous fractures. Each major characteristic is assigned a numerical score based upon injury severity. The sum of these scores constitutes the injury severity score. Conclusions By addressing both discoligamentous integrity and neurological status, the SLIC system may overcome major limitations of earlier classification systems. The system incorporates a number of critical clinical variables-including neurological status, absent in earlier systems-and is simple to apply and may provide both diagnostic and prognostic information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".