Controversies in the Management of Geriatric Odontoid Fractures
Bibliographic record
Abstract
Fractures of the odontoid process of C2 have become increasingly prevalent in the aging population and are typically associated with a high incidence of morbidity. Dens fractures comprise the majority of all cervical fractures in patients older than 80 years and remain the most common cervical fracture pattern in all geriatric patients. Type II odontoid fractures have been associated with limited healing potential, and both nonoperative and operative management are associated with high mortality rates. Historically, there has been some debate in the literature with regards to optimal management strategies to maximize outcomes in geriatric patients. Recent, high-quality evidence has indicated that surgical treatment of type II odontoid fractures in elderly patients is associated with improvements in both short- and long-term mortality. Additionally, surgical intervention has been shown to improve functional outcomes when compared with nonsurgical treatment. Factors to consider before surgery for geriatric type II odontoid fractures include associated comorbidities and the safety of general anesthesia administration. With appropriate measures of patient selection, surgery can provide an efficacious option for geriatric patients with type II odontoid fractures. We recommend surgical intervention via a posterior C1-C2 arthrodesis for geriatric type II odontoid fractures, provided that the surgery itself does not represent an unreasonable risk for mortality.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".