Medical Imaging Resource Center (MIRC) for Veterinary Medicine: A Digital Image Teaching File
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: Veterinary radiology has a need for software to facilitate the creation of digital image teaching files. The Medical Imaging Resource Center (MIRC) is widely used in medicine to create teaching cases and store data from clinical trials. This open-source software was identified as a solution for use in veterinary medicine. METHODOLOGY: The additional fields needed to adapt the system for veterinary use were identified as sex, species, and breed. Breed and species codes from the Matthew J. Ryan Veterinary Hospital of the University of Pennsylvania and from the Standard Nomenclature of Veterinary Diseases and Operations (SNVDO) were gathered and correlated. RESULTS: The sex fields added were male, male neutered, female, and female neutered. The breed and species codes were combined into a single term. These were coded in eXtensible Markup Language (XML) and added to the software's veterinary document template and search capabilities. CONCLUSIONS: MIRC was successfully adapted for use in creating digital teaching files for veterinary medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.027 |
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".