Teaching Tip: Designing Three-Dimensional (3-D) Printed Struvite and Calcium Oxalate Crystals for Microscopic Examination
Bibliographic record
Abstract
Accredited colleges of veterinary medicine are required by the American Veterinary Medical Association (AVMA) Council on Education (COE) to provide learners with hands-on diagnostic method training, including urinalysis. Although teaching hospitals and affiliated clinical partners offer opportunities to test and interpret urine, caseload is unpredictable. Textbook images and published case reports offer substitutes for experiential learning. However, these read-only modalities lack experiences for learners to evaluate slides microscopically for crystalluria. This teaching tip describes the development of three-dimensional (3-D) printed struvite and calcium oxalate models for skills training. Micro-precision 3-D printed models were designed using computer-aided design (CAD) software. Geometric representations in the Standard for the Exchange of Product Data (STEP) file format were exported for 3-D printing on 2 µm resolution platforms. Prints were manufactured from high temperature laminating (HTL) resin and affixed to glass slides. Although these 3-D prints are macroscopic rather than microscopic, their orientation on a fixed template side by side allows learners to differentiate between shapes, a skill that is key to urine sediment examination. These proof-of-concept prototypes will be integrated into the author's pre-clinical curriculum so that learners can gain experience identifying and differentiating between printed struvite, calcium oxalate monohydrate, and calcium oxalate dihydrate models as they would during routine inspection of urine. Formal feedback on the efficacy of these printed models will be solicited from learners and the instructional team. Future iterations will miniaturize the printed models to reflect their real-to-life microscopic dimensions more accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".