COMPARISON OF PLAIN RADIOGRAPHY AND COMPUTED TOMOGRAPHY FOR DETECTION OF ELBOW DYSPLASIA IN LABRADOR RETRIEVER
Bibliographic record
Abstract
To our knowledge there has been no comprehensive direct comparison between the plain radiology and computed tomography (CT) for diagnosis of elbow dysplasia through the use of a grading score. \nThe first aim of the study was to clinically apply a comparative grid (proposed at the last IEWG meeting in 2016) to grade elbow dysplasia on CT similarly as is done on traditional radiology.\nThe second objective was to evaluate the concordance of the grading between the two imaging modalities at the age less than 12 months and over 12 months and to emphasise the differences observed between puppies and adult dogs.\nThirty-nine (39) Labrador Retriever (78 elbow joints) were included in the study, the dogs were own client dogs, asymptomatic, no one showed any sign of lameness nor other clinical problems. \nAt the age <12 months the agreement between plain radiology grading and CT grading was fair (K 0.33), the sensitivity of radiography to identify elbow dysplasia was 75% and specificity 100% (P<0.001). At the age >12months the agreement was fair too with K 0.23 and the sensitivity was 70% and specificity 98% (P<0.001).\nA development of the disease has been noted between the evaluations at different ages. On plain radiography the grade of disease worsened in 24 elbow joint and improved in 2 (P 0.001). On CT the grade of the disease remained invariable in 54 joints and showed a worse grade in 19 joints and improved in 5 joints (P 0.004).\nFurther studies including also other breeds might provide more information about the accurate use of a score grading on CT. The presence of false-negatives in our group of dogs and the fair agreement between the two modalities open a discussion about the exclusive use of radiographs in the screening program. As well a screening program to treat dogs at too young an age should be reassessed having regard to the disease progression, which, however is not related to any clinical symptoms.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".