Computed Tomographic Analysis of Ventral Atlantoaxial Optimal Safe Implantation Corridors in 27 Dogs
Bibliographic record
Abstract
Abstract Objectives Ventral atlantoaxial stabilization techniques are challenging surgical procedures in dogs. Available surgical guidelines are based upon subjective anatomical landmarks, and limited radiographic and computed tomographic data. The aims of this study were (1) to provide detailed anatomical descriptions of atlantoaxial optimal safe implantation corridors to generate objective recommendations for optimal implant placements and (2) to compare anatomical data obtained in non-affected Toy breed dogs, affected Toy breed dogs suffering from atlantoaxial instability and non-affected Beagle dogs. Methods Anatomical data were collected from a prospectively recruited population of 27 dogs using a previously validated method of optimal safe implantation corridor analysis using computed tomographic images. Results Optimal implant positions and three-dimensional numerical data were generated successfully in all cases. Anatomical landmarks could be used to generate objective definitions of optimal insertion points which were applicable across all three groups. Overall the geometrical distribution of all implant sites was similar in all three groups with a few exceptions. Clinical Significance This study provides extensive anatomical data available to facilitate surgical planning of implant placement for atlantoaxial stabilization. Our data suggest that non-affected Toy breed dogs and non-affected Beagle dogs constitute reasonable research models to study atlantoaxial stabilization constructs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".