Computed Tomographic Evaluation of the Sacroiliac Joints of Young Working Labrador Retrievers of Various Work Status Groups: Detected Lesions Vary Among the Different Groups and Finite Element Analyses of the Static Pelvis Yields Repeatable Measures of Sacroiliac Ligament Joint Strain
Bibliographic record
Abstract
Musculoskeletal injuries can lead to a working dog being withdrawn from service prior to retirement. During training exercises, young working dogs are often required to perform repetitive tasks, including adoption of an upright posture (or “hupp” task). Noninvasive, quantitative methods would be helpful for supporting research on effects of these repetitive tasks on sacroiliac joints (SIJ). Furthering our understanding of lesions in and biomechanical stresses on the SIJ could provide insight into possible training modifications for minimizing risks of SIJ injury. Aims of this retrospective, secondary analysis, exploratory study were to test hypotheses that 1) mean numbers of SIJ computed tomographic (CT) lesions/dog would differ among work status groups in young working Labrador Retrievers; 2) a methodology for using CT data and finite element analysis (FEA) to quantify SIJ ligament strain in the static canine pelvis would be feasible; and 3) this FEA methodology would yield repeatable measures of SIJ ligament strain. Clinical and CT data for 22 Labrador retriever working dogs, aged 11-48 months, were retrospectively reviewed. Dogs were categorized into three work status groups (Breeder, Detection, Other). A veterinary radiologist who was unaware of dog group status recorded numbers of CT lesions for each SIJ, based on previously published criteria. Mean numbers of SIJ CT lesions/dog were compared among dog work status groups. An a priori FEA model was created from the CT images of one of the dogs using image analysis software packages. Using tissue properties previously published for the human pelvis, various directional loads (n=8) and forces (48 ligament strain values) were placed on the canine model in five trials. Repeatability was tested using regression analysis. There was a significantly greater mean number of subchondral sclerosis lesions in left SIJ of Breeder versus Detection dogs, a significantly greater mean number of subchondral cysts in right SIJ for Detection versus Breeder dogs, and a significantly greater mean number of subchondral cysts in right SIJ of Other versus Breeder dogs (p<0.05). Finite element modeling and analysis using CT data was feasible and yielded repeatable results in 47/48 (98%) of tests at each combination of strain, ligament, and side.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".