Comparative evaluation of computed radiography and computed tomography for the diagnosis of thoraco-abdominal disorders in dogs
Bibliographic record
Abstract
A total of four cases with an age range of 4-10 years old of various breeds of two male labradors, one female Golden Retriever, and one male Great Dane presented with a history and clinical signs of thoraco-abdominal disorders such as inappetence coughing, inappetence, respiratory distress, vomission, high temperature, tachycardia, cardiac murmurs, muffled lung sounds and distended abdomen presented to Veterinary hospital, Veterinary College, Hassan. Further, these pets were subjected to Computed Radiographic (CR) and Computed Tomographic (CT) evaluation. These modalities helped to diagnose the thoraco-abdominal disorders, such as dilated cardiomyopathy (DCM), pulmonary oedema and pleural effusion secondary to DCM and associated with gastric dilatation and volvulus (GDV) in a Grate Dane, soft tissue sarcoma at the axial region of the left forelimb and associated with hepatocellular carcinoma (HCC) in a Labrador, lymphoma (abdominal lymph nodal mass) also associated with ascites and pleural effusion secondary to cardiac insufficiency in a Golden Retriever, nasal adenocarcinoma associated with pulmonary metastasis and GIST in a labrador. However, radiography may be the first choice for diagnosing thoracoabdominal disorders as it is easily accessible and low-cost compared to CT. If the cases have ambiguity in diagnosis, they need to be subjected to CT for confirmative diagnosis. CT was a more useful modality, resulting in a high-detailed anatomical image with excellent soft tissue contrast of the thoraco-abdomen, facilitating the characterisation and localisation of the thoraco-abdominal lesions. The precision offered by both modalities facilitated the planning, surgical and medical management, and established the prognosis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".