Comparison of Radiography and Ultrasound for Diagnosis of Rib Fractures in Canine Cadavers
Bibliographic record
Abstract
Human studies suggest point-of-care ultrasound (POCUS) is superior to radiographs for diagnosing rib fractures, but its efficacy in veterinary medicine remains unclear. This study aimed to compare the sensitivity and specificity of POCUS and digital radiographs for detecting rib fractures in canine cadavers, using necropsy as the reference standard. Nine canine cadavers were randomly assigned to either a fracture or control group, with fractures created surgically. Blinded evaluations were performed by an expert and novice sonographer, as well as a board-certified radiologist and a novice radiograph interpreter. Sensitivity and specificity for detecting rib fractures were 83% and 99.74% for ultrasound and 82% and 99.22% for radiographs, with no significant difference between modalities. However, the time required to identify rib fractures varied significantly, with ultrasound taking considerably longer than radiograph interpretation. The expert and novice sonographers required an average of 26 and 64 min, respectively, whereas the radiologist and novice radiograph interpreter took 3 and 10 min. These findings suggest that POCUS and radiographs provide comparable accuracy in detecting rib fractures in canine cadavers. Excluding the time required to obtain radiographs, ultrasound takes longer than radiograph interpretation to identify rib fractures. While POCUS remains a valuable diagnostic tool, its practicality in a clinical setting needs further investigation.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".