Inter-rater agreement and characterization of pleural line and subpleural fields in canine lung ultrasound: a comparative pilot study between high-frequency linear and curvilinear transducers using B- and M-mode ultrasonographic profiles
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Bibliographic record
Abstract
BACKGROUND: Lung ultrasound (LUS) is increasingly utilized in veterinary medicine to assess pulmonary conditions. However, the characterization of pleural line and subpleural fields using different ultrasound transducers, specifically high-frequency linear ultrasound transducers (HFLUT) and curvilinear transducers (CUT), remains underexplored in canine patients. This study aimed to evaluate inter-rater agreement in the characterization of pleural line and subpleural fields using B- and M-mode ultrasonography in dogs with and without respiratory distress. RESULTS: Eighty-eight ultrasound clips from nine dogs were analyzed. HFLUT demonstrated strong inter-rater agreement in B-mode (κ = 0.89) and near-perfect agreement in M-mode (κ = 1.00) for pleural line homogeneity. In contrast, CUT showed minimal agreement in both B-mode (κ = 0.34) and M-mode (κ = 0.37). Homogeneous pleural lines were predominantly observed in control dogs or those with cardiogenic pulmonary edema (CPE), while non-homogeneous pleural lines were more common in dogs with non-cardiogenic alveolar-interstitial syndrome (NCAIS). Vertical subpleural fields identified in M-mode were associated with both CPE and NCAIS, whereas horizontal fields were more often observed in control dogs. CONCLUSIONS: HFLUT offers superior inter-rater reliability for characterizing pleural and subpleural features in canine LUS compared to CUT, particularly in M-mode. These findings suggest HFLUT may enhance diagnostic accuracy for pulmonary conditions in dogs. Further studies are needed to explore the diagnostic potential of LUS in differentiating vertical artifact (e.g., B-lines) etiologies in veterinary patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it