Choosing the optimal combination of lung lobe evaluation during focused pulmonary ultrasonography in calves
Bibliographic record
Abstract
Bovine respiratory disease (BRD) poses significant challenges on beef and dairy farms, affecting mortality rates, animal welfare, and production efficiency. Although pulmonary ultrasonography is highly sensitive and specific for monitoring lung lesions and diagnosing BRD, its practical application could be optimized by focusing on the most commonly affected lung lobes. This study first evaluated the efficacy of focused lung ultrasonography for diagnosing BRD in calves, examining individual lung lobes and their associations versus the extensive lung scanning under various disease prevalence scenarios. Then, the relationship between individual and combined clinical respiratory signs versus lung consolidation was analyzed. In a combined analysis, 193 Holstein calves from a longitudinal study and 112 Angus calves from a cross-sectional study underwent a total of 1,265 complete bilateral thoracic ultrasonographic evaluations from 1 to 6 mo of age. Then, the agreement and sensitivity (Se) of specific lung lobe combinations compared with the findings from total lung ultrasonography was assessed. The classification and regression tree (CART) algorithm was used to suggest an optimal examination sequence, and logistic regression was applied to associate specific clinical signs with the presence of lung consolidation, adjusting for breed and calf age. Findings revealed that the most sensitive areas are the cranial (kappa value [κ] = 0.867; Se = 84.7%) and caudal portion of the right cranial lobe (κ = 0.433; Se = 40.3%), the caudal portion (κ = 0.235; Se = 20.6%) of the left cranial lobe, and the middle lobe (κ = 0.25; Se = 22%). The optimal lobe combinations for focused lung ultrasonography were identified as the right cranial lobe paired with either the left cranial lobe or the middle lobe. Focused techniques achieved Se greater than 94% and maintained good agreement. These focused techniques were relatively robust to various true lung consolidation scenarios. The CART analysis recommended initiating examinations with the right cranial lobe, proceeding to the left cranial lobe, and concluding with the middle lobe. Although spontaneous cough was linked to pneumonia presence, reliance on a single clinical sign is not advised due to low Se (26.8%) and high specificity (85.4%); it should merely prompt further ultrasound assessment. In conclusion, focused lung ultrasonography, especially utilizing the right cranial lobe in conjunction with the left cranial lobe or the middle lobe, emerged as an effective strategy for focused pulmonary ultrasonography, preserving the accuracy of the results.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".