The management of odontoid fractures through the lens of evolution in classification schemes: A systematic review with illustrative case examples
Bibliographic record
Abstract
Introduction: Odontoid fractures account for approximately 15% of all cervical spine fractures. Despite numerous classification systems, controversy persists regarding the ideal treatment of these fractures, especially in elderly and medically frail patients. Research question: This article aims to provide a systematic review of odontoid fracture classifications and assess their clinical applicability. Material and methods: A systematic literature review was conducted in PubMed, Embase, and Cochrane databases using the terms "odontoid", "fracture", and "classification". Articles published between 1974 and 2024 were analyzed and those containing odontoid fracture classifications were included. Results: Four hundred and fifty-seven articles were identified, and 32 were selected for detailed investigation. Seven articles were selected after reviewing the full text, and four additional articles cited in the references were included, from which two were published before 1974. A total of eleven classification systems were found in the literature. The classifications were based on the position and direction of the fracture line, displacement, angulation, embryology, and odontoid anatomy. The AO Spine Classification System was among the more recent frameworks reviewed and analyzes the presence of ligamentous injury or translation. Discussion and conclusions: Anderson and D'Alonzo, Roy-Camille, Grauer, and the AO Spine Classification System are the most commonly applied in clinical practice. However, existing systems lack specific considerations for osteoporosis and the medical frailty of elderly patients, who constitute a substantial portion of cases. Future classification systems should address these factors to better guide treatment for this population.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".