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
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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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 | 0.040 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".