The Subaxial Cervical AO Spine Injury Score
Bibliographic record
Abstract
STUDY DESIGN: Global cross-sectional survey. OBJECTIVE: To develop an injury score for the AO Spine Subaxial Cervical Spine Injury Classification System. METHODS: Respondents numerically graded each variable within the classification system for severity. Based on the results, and with input from the AO Spine Trauma Knowledge Forum, the Subaxial Cervical AO Spine Injury Score was developed. RESULTS: An A0 injury was assigned an injury score of 0, A1 a score of 1, and A2 a score of 2. Given the significant increase in severity, A3 was given a score of 4. Based on equal severity assessment, A4 and B1 were both assigned a score of 5. B2 and B3 injuries were assigned a score of 6. Unstable C-type injuries were given a score of 7. Stable F1 injuries were assigned a score of 2, with a 2-point increase for F2 injuries. Likewise, F3 injuries received a score of 5, whereas more unstable F4 injuries a score of 7. Neurologic status severity rating scores increased stepwise, with scores of 0 for N0, 1 for N1, and 2 for N2. Consistent with the Thoracolumbar AO Spine Injury Score, N3 (incomplete) and N4 (complete) injuries were given a score of 4. Finally, case-specific modifiers M1 (PLC injury) received a score of 1, while M2 (critical disc herniation) and M3 (spine stiffening disease) received a score of 4. CONCLUSIONS: The Subaxial Cervical AO Spine Injury Score is an easy-to-use metric that can help develop a surgical algorithm to supplement the AO Spine Subaxial Cervical Spine Injury Classification System.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".