Addressing the challenges and controversies of managing os odontoideum: results of a systematic review
Bibliographic record
Abstract
INTRODUCTION: Os odontoideum is a rare condition with a controversial pathogenesis and poorly understood natural history. As a result, it is difficult for clinicians to predict which patients require surgical fusion to prevent symptomatic progression and potentially devastating neurologic injury. METHODS: We undertook a systematic review of the literature to evaluate the clinical outcomes in the treatment of asymptomatic and symptomatic cases of os odontoideum. Pubmed, EMBASE, Cochrane, National Guideline Clearinghouse Databases as well as bibliographies of key articles were searched. Articles were reviewed by two independently working reviewers. Inclusion and exclusion criteria were set and each article was subject to a predefined quality rating scheme. RESULTS: We identified eleven articles meeting our inclusion criteria. Seven studies included patients with asymptomatic os odontoideum discovered incidentally from which it was possible to examine data on 18 individuals. Six studies were identified containing more than 15 patients with symptomatic os odontoideum. CONCLUSION: The existing literature regarding both asymptomatic or symptomatic os odontoideum and its treatment is very limited. No definitive conclusions can be drawn from these studies. A basic approach to the management of os odontoideum is offered based on the findings of this study.
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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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".