AOSpine Subaxial Cervical Spine Injury Classification System
Bibliographic record
Abstract
Introduction The purpose of this project is to describe a morphology-based subaxial cervical traumatic injury classification system. Similar to the effort put toward the thoracolumbar system, the goal was to develop a comprehensive yet simple classification system with high intra and interobserver reliability to be used for clinical and research purposes. Material and Methods A subaxial cervical spine injury classification system was developed using a consensus process. All investigators were required to successfully grade 10 cases to demonstrate comprehension of the system before grading 30 additional cases on two occasions, 1 month apart. Kappa coefficients ( κ) were calculated for interobserver and intraobserver reliability. Results The classification system is based on the following three injury types: compression injuries (A), tension band injuries (B), and translational injuries (C), with additional descriptions for facet injuries, as well as patient-specific modifiers and neurologic status. Interobserver and intraobserver reliability was substantial for all injury subtypes ( κ = 0.64 and 0.75, respectively). Conclusion The AOSpine subaxial cervical spine injury classification system demonstrated substantial reliability in this initial assessment, and could be a valuable tool for patient care and for research purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".