Phenotyping of lumbosacral stenosis in Labrador retrievers using computed tomography
Bibliographic record
Abstract
Abstract Deep phenotyping tools for characterizing preclinical morphological conditions are important for supporting genetic research studies. Objectives of this retrospective, cross‐sectional, methods comparison study were to describe and compare qualitative and quantitative deep phenotypic characteristics of lumbosacral stenosis in Labrador retrievers using computed tomography (CT). Lumbosacral CT scans and medical records were retrieved from data archives at three veterinary hospitals. Using previously published qualitative CT diagnostic criteria, a board‐certified veterinary radiologist assigned dogs as either lumbosacral stenosis positive or lumbosacral stenosis negative at six vertebral locations. A second observer independently measured vertebral canal area, vertebral fat area, and vertebral body area; and calculated ratios of vertebral canal area/vertebral body area and vertebral fat area/vertebral body area (fat area ratio) at all six locations. Twenty‐five dogs were sampled (lumbosacral stenosis negative, 11 dogs; lumbosacral stenosis positive, 14 dogs). Of the six locations, cranial L6 was the most affected by lumbosacral stenosis (33%). Five of six dogs (83%) with clinical signs of lumbosacral pain were lumbosacral stenosis positive at two or more levels. All four quantitative variables were significantly smaller at the cranial aspects of the L6 and L7 vertebral foramina than at the caudal aspects ( P < 0.0001). Fat area ratio was a significant predictor of lumbosacral stenosis positive status at all six locations with cranial L6 having the greatest predictive value ( R 2 = 0.43) and range of predictive probability (25–90%). Findings from the current study supported the use of CT as a deep phenotyping tool for future research studies of lumbosacral stenosis in Labrador retrievers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".