Ex vivo comparison of pin placement with patient‐specific drill guides or freehand technique in canine cadaveric spines
Bibliographic record
Abstract
OBJECTIVE: To compare vertebral implant placement in the canine thoracolumbar spine between 3D-printed patient-specific drill guides (3DPG) and the conventional freehand technique (FH). STUDY DESIGN: Ex vivo study. ANIMALS: Cadaveric canine spines (n = 24). METHODS: Implant trajectories were established for the left and right sides of the T10 through L6 vertebrae based on computed tomography (CT) imaging. Customized drill guides were created for each vertebra of interest. Each cadaver was randomly assigned to one of six veterinarians with varying levels of experience placing vertebral implants. Vertebrae were randomly assigned a surgical order and technique (3DPG or FH) for both sides. Postoperative CT images were acquired. A single, blinded observer assessed pin placement using a modified Zdichavsky classification. RESULTS: A total of 480 implants were placed in 240 vertebrae. Three sites were excluded from the analysis; therefore, a total of 238 implants were evaluated using the FH technique and 239 implants using 3DPG. When evaluating implant placement, 152/239 (63.6%) of 3DPG implants were considered to have an acceptable placement in comparison with 115/248 (48.32%) with FH. Overall, pin placement using 3DPG was more likely to provide acceptable pin placement (p < .001) in comparison with the FH technique for surgeons at all levels of experience. CONCLUSION: The use of 3DPG was shown to be better than the conventional freehand technique regarding acceptable placement of implants in the thoracolumbar spine of canine cadavers. CLINICAL SIGNIFICANCE: Utilizing 3DPG can be considered better than the traditional FH technique when placing implants in the canine thoracolumbar spine.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".