Thermal Imaging of Normal and Cranial Cruciate Ligament‐Deficient Stifles in Dogs
Bibliographic record
Abstract
OBJECTIVE: To investigate the capability of thermography for differentiation between normal stifles and those with cranial cruciate ligament (CCL) rupture in dogs, initially with a full hair coat and 1 hour after clipping the hair coat. STUDY DESIGN: Prospective study. ANIMALS: Labrador Retrievers (n=6) with normal stifle joints (controls) and adult dogs (n=10) with CCL rupture. METHODS: Thermography was performed before, and 60 minutes after, clipping the hair coat from the pelvic limb. Stifle images were classified as normal or abnormal, then subclassified as clipped and unclipped hair coat. CCL deficiency was confirmed at surgery and thermographic images subsequently classified as abnormal before analysis with image processing software. RESULTS: Using image recognition analysis, differentiation between normal and CCL-deficient stifles in both clipped and unclipped dogs was 85% successful on cranial images, medial, caudal, and lateral images were between 75% and 85% successful. Although there were significant increases in skin temperature after clipping in both groups (P<.0002-.0001), there were no significant temperature differences between normal and CCL-deficient stifles when the entire stifle was examined. CONCLUSION: Thermography was successful in differentiating naturally occurring CCL-deficient stifles in dogs, with a success rate of 75-85%. Clipping is not necessary for successful thermographic evaluation of the canine stifle. CLINICAL RELEVANCE: Thermography may be a useful imaging modality for diagnosis of CCL deficiency in dogs when CCL rupture is suspected but stifle laxity is not evident.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".