The Use of Multi–detector Row Computed Tomography (MDCT) as an Alternative to Specimen Preparation for Anatomical Instruction
Bibliographic record
Abstract
The purpose of the study reported here was to establish a method of teaching veterinary anatomy, including radiologic anatomy, for clinical practice using computer-aided diagnosis (CAD). Two clinically healthy dogs and three cats were scanned using multi-detector row computed tomography (MDCT). Images were made by means of imaging processing software. At the workstation, by observing the transverse, dorsal-plane, or sagittal sections and three-dimensional (3D) images simultaneously, it is much easier to understand the 3D anatomical structure. With this educational support system, anatomical figures can be explained using living animals instead of specimens. In addition, clinical representative examples can be used to show anatomical disorders to students. Veterinary students (N = 62) who filled out a questionnaire evaluating how the method aided their understanding of both experimental study and clinical examples gave it a score of 88.2 +/- 20.6 (Mean +/- SD) on a visual analog scale. This system can enhance veterinary students' understanding and interest in anatomy and can enable us to offer them a quality veterinary medical education. We concluded that CAD is a useful new option not only for clinical service but also for veterinary education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".