RADIOGRAPHIC, COMPUTED TOMOGRAPHIC, AND ULTRASONOGRAPHIC FINDINGS WITH MIGRATING INTRATHORACIC GRASS AWNS IN DOGS AND CATS
Bibliographic record
Abstract
The purpose of this study was to describe the clinical, radiographic, and computed tomographic findings in dogs and cats with migrating intrathoracic grass awns. Thirty-five dogs and five cats with visual confirmation of a grass awn following surgery, endoscopy or necropsy, and histology were assessed. The medical records and all diagnostic imaging studies were reviewed retrospectively. Labrador Retrievers or English Pointers < 5 years of age, with a history of coughing and hyperthermia, were the most common presentations. Seventeen animals had an inflammatory leukogram of which 14 had a left shift or toxic neutrophils. Radiographs were performed in 38 animals and computed tomography (CT) in 14. Thoracic radiographs were characterized by focal pulmonary interstitial to alveolar opacities (n = 26) that occurred most commonly in the caudal (n = 19) or accessory lobes (n = 8). Additional findings included pneumothorax (n = 9), pleural effusion (n = 8), and pleural thickening (n = 7). Pulmonary opacities identified on radiographs correlated to areas of pneumonia and foreign body location. CT findings included focal interstitial to alveolar pulmonary opacities (n = 12) most commonly in the right caudal lung lobe (n = 9), pleural thickening (n = 11), mildly enlarged intrathoracic lymph nodes (n = 10), soft tissue tracking (n = 7) with enhancing margins (n = 4), pneumothorax (n = 6), pleural effusion (n = 4), and foreign body visualization (n = 4). Histologic diagnoses included pulmonary and mediastinal granulomas or abscesses, bronchopneumonia, and pleuritis. Migrating intrathoracic grass awns should be considered as a differential diagnosis in coughing, febrile animals with focal interstitial to alveolar pulmonary opacities, pleural effusion, pleural thickening, and/or pneumothorax on radiographs or CT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".