Treatment of Injuries to the Subaxial Cervical Spine: Recommendations of the Spine Section of the German Society for Orthopaedics and Trauma (DGOU)
Bibliographic record
Abstract
STUDY DESIGN: Expert consensus. OBJECTIVES: To establish treatment recommendations for subaxial cervical spine injuries based on current literature and the knowledge of the Spine Section of the German Society for Orthopaedics and Trauma. METHODS: This recommendation summarizes the knowledge of the Spine Section of the German Society for Orthopaedics and Trauma. RESULTS: Therapeutic goals are a stable, painless cervical spine and protection against secondary neurologic damage while retaining maximum possible motion and spinal profile. The AOSpine classification for subaxial cervical injuries is recommended. The Canadian C-Spine Rule is recommended to decide on the need for imaging. Computed tomography is the favoured modality. Conventional x-ray is preserved for cases lacking a "dangerous mechanism of injury." Magnetic resonance imaging is recommended in case of unexplained neurologic deficit, prior to closed reduction and to exclude disco-ligamentous injuries. Computed tomography angiography is recommended in high-grade facet joint injuries or in the presence of vertebra-basilar symptoms. A0-, A1- and A2-injuries are treated conservatively, but have to be monitored for progressive kyphosis. A3 injuries are operated in the majority of cases. A4- and B- and C-type injuries are treated surgically. Most injuries can be treated with anterior plate stabilization and interbody support; A4 fractures need vertebral body replacement. In certain cases, additive or pure posterior instrumentation is needed. Usually, lateral mass screws suffice. A navigation system is advised for pedicle screws from C3 to C6. CONCLUSIONS: These recommendations provide a framework for the treatment of subaxial cervical spine Injuries. They give advice about diagnostic measures and the therapeutic strategy.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".