Computed Tomography and Biomechanical Comparison between Trans-Articular Screw Fixation and 2 Polymethylmethacrylate Cemented Constructs for Ventral Atlantoaxial Stabilization
Bibliographic record
Abstract
OBJECTIVES: Canine ventral atlantoaxial stabilization methods have been constantly evolving over the past few decades. Yet, proper experimental data comparing the feasibility and biomechanical properties of currently available surgical options are lacking. The aims of this study were (1) to describe and compare the safety profiles and biomechanical properties of three ventral atlantoaxial stabilization methods; and (2) to test whether recently reported optimal implant definitions constitute reasonable guidelines. METHODS: Three types of atlantoaxial stabilization including trans-articular screw fixation (TSF) and two cemented constructs (MI5 and MI6) were performed in 21 Beagle cadavers. Post-surgical computed tomography (CT) images of the constructs and biomechanical data were then generated and statistically analysed. RESULTS: The CT data revealed that TSF achieved significantly better apposition than cemented constructs. Out of 91 screws positioned, 4.4% were graded as dangerous and 86.8% as optimal. Optimal positioning was most challenging to obtain for mono-cortical screws. Analysis of biomechanical data suggested that all three techniques could likely achieve similar rates of atlantoaxial fusion when submitted to physiological loads but also that cemented constructs were less prone to failure compared with TSF. CLINICAL SIGNIFICANCE: This study provides evidence that all three techniques are technically feasible and biomechanically viable but also that the evaluated surgical guidelines could be improved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".