The Surgical Approach to Subaxial Cervical Spine Injuries
Bibliographic record
Abstract
STUDY DESIGN: Systematic review of literature and expert clinical opinions of the members of the Spine Trauma Study Group were combined to develop and refine this algorithm. OBJECTIVE: To develop an evidence-based algorithm for surgical approaches to manage subaxial cervical injuries using a systematic review of the literature, expert opinion, and anticipated patient preferences. SUMMARY OF BACKGROUND DATA: There is lack of consensus in the management of subaxial cervical spine trauma, in part, because of the lack of a clinically relevant system for classifying these injuries. The newly developed Subaxial Injury Classification scoring system categorizes injury morphology into 3 broad groups, includes an assessment of the integrity of the discoligamentous soft tissue structures and the patient's neurologic status, and thus guides surgical or nonsurgical treatment. The choice of a specific surgical technique and approach is currently not evidence based, and this gap in knowledge is one which the current article seeks to address. METHODS: A literature review followed by a consensus of experts approach was used to develop the algorithm and to ensure face and content validity. RESULTS: An algorithm is presented to guide the choice of surgical approach in cervical subaxial burst fractures, distraction injuries, and translation or rotation injuries. The burst or compression injuries and distraction injuries are more likely to be treated with a single anterior approach, whereas the more severe translation or rotation injuries may more commonly be approached posteriorly or with combined anterior and posterior surgery. CONCLUSION: This algorithm; derived from the Subaxial Injury Classification scoring system, will assist surgeons in answering the 2 most common questions they face when managing subaxial cervical spine trauma: "Should I operate?" and "Which surgical approach should I select?"
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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 | 0.042 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.031 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".