Development and Validation of a Three-Dimensional Printed Training Model to Teach Ultrasound-Guided Injections of the Cervical Articular Process Joints in Horses
Bibliographic record
Abstract
Intra-articular injections are routinely performed to alleviate pain and inflammation associated with osteoarthritis in horses. Intra-articular injections require accurate needle placement to optimize clinical outcomes and minimize complications. This study’s objectives were to develop and validate a three-dimensional (3D) printed model of an equine cervical articular process joint to teach ultrasound-guided injections. Five identical models of an equine cervical articular process joint were 3D printed and embedded in 10% ballistic gelatin. Experts’ and novices’ ability to successfully insert a needle into the joint space of the model using ultrasound guidance was assessed and graded using an objective structured clinical examination (OSCE). Scores from experts and novices were compared to evaluate the construct validity of the model. Participants also answered a survey assessing the face and content validity of the model. Experts required less time (22.51 seconds) for correct needle placement into the model joint space than novices (35.96 seconds); however, this difference was not significant ( p = .53). Experts’ median total OSCE score (14) was significantly higher ( p = .03) than novices’ (12), supporting the model’s construct validity. Participants agreed on the face and content validity of the model by grading all survey questions greater than 7 on a 10-point Likert-type scale. In summary, we successfully developed a 3D printed model of an equine cervical articular process joint, partially demonstrated the construct validity of the model, and proved the face and content validity of this new training tool.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".