Surgery vs conservative treatment for type II and III odontoid fractures in a geriatric population
Bibliographic record
Abstract
BACKGROUND: It is unclear whether surgery or conservative treatment is more suitable for elderly patients with type II and type III odontoid fractures. We performed this meta-analysis to compare the efficacy of surgical and conservative treatments for type II and type III odontoid fractures. METHODS: A literature search was performed in PubMed, Embase, Web of Science, and Cochrane Library in January 2017. Only articles comparing surgery with conservative treatment in elderly patients with type II and type III odontoid fractures were selected. After 2 authors independently assessed the retrieved studies, 18 articles were included in this meta-analysis, and the primary endpoints were the nonunion rate and mortality rate. The secondary outcomes were patient satisfaction, complications, and the length of the hospital stay. The quality of the included studies was evaluated using the modified Newcastle-Ottawa scale. Sensitivity analyses were performed for high-quality studies, and the publication bias was evaluated using a funnel plot. RESULTS: Lower nonunion (odds ratio [OR]: 0.27, 95% confidence interval [CI]: 0.18-0.40, P < .05) and mortality rates (OR: 0.52, 95% CI: 0.34-0.79, P < .05) confirmed the superiority of surgery in treating type II and type III fractures. The secondary outcomes differed. Patients in the surgery group felt more satisfied with the outcome (OR: 3.44, 95% CI: 1.19-9.95, P < .05), and the complications were similar in the 2 groups (OR: 1.14, 95% CI: 0.78-1.68, P = .5), whereas patients in conservative groups spent less time in the hospital (OR: 5.10, 95% CI: 2.73-7.47, P < .05). The results of the subgroup analyses and sensitivity analysis were similar to the original outcomes, and no obvious publication bias was observed in the funnel plot. CONCLUSION: Most elderly (younger than 70 years) patients with type II or type III odontoid fractures should be considered candidates for surgical treatment, due to the higher union rate and lower mortality rate, while statistically significant differences were not observed in the population with an advanced age (older than 70 years). Therefore, the selection of the therapeutic approach for elderly patients with odontoid fractures requires further exploration. Simultaneously, based on our meta-analysis, a posterior arthrodesis treatment was significantly superior to the anterior odontoid screw 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".