Digital Radiographic Analysis of Optical Density of the Distal Segment of the Trochlear Notch of the Ulna in Labrador Retrievers with Fragmented Coronoid Process
Bibliographic record
Abstract
The aim of the study was to find whether there is a difference in the optical density of the subtrochlear region of incisura trochlearis and in the region of processus coronoideus medialis ulnae in elbow joints with fragmented processus coronoideus and in healthy elbow joints of the Labrador retriever breed. We evaluated digital radiographs of elbows (n = 26) with arthroscopically or arthrotomically proven FCP and digital radiographs of healthy elbows (n = 28). A template was made on radiographs in the JiveX program (Visus Technology Transfer) demarcating individual regions of interest (ROI) in which median optical density was measured. For normalisation of median optical density data of individual ROI, median optical density of the caudal ulnar cortex was used. Elbow joints with fragmented processus coronoideus had a lower mean median optical density in the distal part of incisura trochlearis compared to healthy elbow joints. The lowest median optical densities were found in the region of processus coronoideus medialis and in the distal part of the trochlear notch of the ulna in the region of processus coronoideus lateralis. The biggest difference in median optical densities between elbows with FCP and healthy elbows was found in regions distant from the articular surface. In evaluation of the opacity of the trochlear notch of the ulna it is appropriate to assess the whole region of the proximal ulnar metaphysis from the articular surface to the caudal ulnar cortex.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".