The prevalence of sacroiliac joint CT and MRI findings is high in large breed dogs
Bibliographic record
Abstract
Sacroiliac joint (SIJ) disease has been described as one of the possible causes of lumbosacral (LS) region pain in dogs. However, published information is currently lacking for the computed tomographic (CT) and magnetic resonance imaging (MRI) characteristics of canine SIJ disease. The objectives of this retrospective, observational study were to describe and quantify CT and MRI SIJ findings in a sample of large breed dogs and test associations between the numbers of SIJ findings and other variables. Data archives for a veterinary teaching hospital were searched for large breed dogs (≥ 22.7 kg) that had CT or MRI scans of the LS and pelvic regions in 2015-2019. Dogs with a history of acute trauma or scans with incomplete SIJs were excluded. A veterinary student recorded medical record findings. A veterinary radiologist and graduate student recorded CT and MRI findings based on previously published criteria in dogs and humans. Fifty-three dogs were sampled (20 CT, 33 MRI). Categories of findings with the highest prevalence were subchondral erosion (100% CT, 100% MRI) and subchondral sclerosis (95% CT, 97% MRI). The total numbers of SIJ findings per dog were not associated with dog age, sex, weight, or concurrent findings in the LS or pelvic regions. The total number of MRI SIJ findings per dog differed between German Shepherds and Labrador Retrievers (P = 0.0237) as well as between Labrador Retrievers and other breeds (P = 0.0414). These results indicated that CT and MRI findings reported in humans with SIJ disease are common in large breed dogs.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".