Retrospective Evaluation of Subpleural Consolidations Using Lung Ultrasound in 634 Dogs and 347 Cats
Bibliographic record
Abstract
Background: Human literature describes vascular patterns in various types of lung consolidations; however, these changes have not been analyzed in dogs and cats. This retrospective analysis of medical records aimed to describe vascular patterns observed in the airless subpleural lung tissue of dogs and cats and to compare these patterns to various lung consolidations and clinical diagnoses according to parenchymal and vascular criteria described in human literature. Animals: This study included 347 cats and 634 dogs. Methods: This was a retrospective study encompassing medical records obtained between 2018 and 2023. Lung ultrasound was performed in dogs and cats and cases with different sonographically identified airless subpleural lung tissue were selected. Airless subpleural lung tissue was categorized into five types of consolidations: shred, nodule, wedge sign, mass, and tissue sign. Further classification was based on the parenchymal criteria, the presence or absence of bronchograms and the vascular patterns identified within these regions. Bronchograms were classified as air bronchograms (dynamic and/or static), fluid bronchograms, mixed fluid and air bronchograms, and/or absence of bronchograms. Vascular patterns were classified as tree-like, residual, chaotic, “vascular sign”, and whether they were continuous with and extended from the chest wall or not. Conclusions and Clinical Importance: It is possible to identify and characterize airless subpleural lung tissue in cats and dogs based on the parenchymal and vascular characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".