Subaxial injury classification system to determine the surgical approach for subaxial cervical spine injuries
Bibliographic record
Abstract
Purpose of review The lack of consensus that exists, relating to the management of subaxial cervical spine trauma, is in part due to the lack of a clinically relevant system for classifying these injuries. Furthermore, there are no guidelines to assist the surgeon in choosing a specific surgical technique and approach for these injuries. The recent development of the subaxial injury classification system and recently published evidence-based algorithms for surgical approaches assist the surgeon in the management of subaxial cervical injuries. Recent findings The newly developed subaxial injury classification scoring system categorizing injury morphology into three broad groups, includes an assessment of the integrity of the discoligamentous soft-tissue structures and the patient's neurological status and thus determines surgical or nonsurgical treatment. A review of the recent literature was used to develop and refine an algorithm for the surgical treatment of subaxial cervical injuries. Summary The burst or compression 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. Controversy still exists in the management of subaxial cervical trauma; however, recent publications provide evidence to guide treatment.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".