Evaluation of a patient‐specific <scp>3D</scp> ‐printed guide for ventral slot surgery in dogs: An ex vivo study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the accuracy of ventral slot creation in canine cadavers with a three-dimensional (3D)-printed drill guide compared to the freehand technique. STUDY DESIGN: Ex vivo study. SAMPLE POPULATION: Eight canine cadavers (23.4-39.8 kg). METHODS: Computed tomography (CT) data was used to create patient-specific 3D-printed surgical guides for ventral slot creation. Intervertebral sites were randomized to undergo either a guided (n = 12) or freehand (n = 12) ventral slot by a novice surgery resident. Postoperative CT images were used to compare ventral slot dimensions, shape, and position. RESULTS: Free-hand ventral slots were significantly shorter than the intended dimensions (p < .01). Dimensions of the guide-assisted ventral slots were not statistically different from the planned dimensions (p = .88, p = .72). Use of the guides resulted in improved accuracy for ventral slot positioning relative to midline and slot shape (difference in coefficient of variations, 32%, and 40%, respectively). CONCLUSION: Ventral slot dimensions were more accurate when created with the patient-specific 3D-printed guide compared to the freehand technique. CLINICAL SIGNIFICANCE: Use of a 3D-printed patient specific surgical guide improves accuracy of ventral slot creation in canine cadavers and improves surgical precision when used by a single novice surgical resident. The results of this study support evaluation of the guides in small breed cadavers and live patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".