Utilisation d'images tomodensitométriques et de reconstructions 3D du thorax du chien comme support pédagogique de l'apprentissage de l'anatomie et de la radiographie thoracique
Bibliographic record
Abstract
Anatomy is one of the cornerstone in the veterinary students’ learning. Therefore it must be complete and fitted to the students. Directly derived from this knowledge, medical imaging is also transdisciplinary. The most common tool at the hand of the vet praticians certainly is radiography. Nonetheless, its great popularity does not involve an easy understanding of the information it gives. Increasingly democratized in veterinary clinics, tomodensitometry allows cross-sectionnal views and tridimensionnal reconstructions. It constitutes an exceptionnal way to visualize and deeply understand topography and the bounds anatomic formations know between them. Tomodensitometry is a wonderful pedagogic tool for the learning of anatomy but also radiography as both of thediagnostic imaging tools have common physics principles. A six years old Labrador Retriever was CT-scanned in the thorax region. Producted images were then edited. Our work allowed us to create 14 videos using tomodensitometric images and tridimensional reconstructions of the thorax region of a dog. Created pedagogic tools are designed for the learning of the thorax anatomy for seven videos, the other seven are designed to help clarify common misleadings in the radiographic reading of the thorax area. All of this ressources are available on the veterinary students’ website for them to peruse
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".