Invited review: Lung ultrasonography—Improving our understanding and management of respiratory disease in young calves
Bibliographic record
Abstract
Lung ultrasonography (LUS) has emerged as an on-farm tool that can rapidly characterize pulmonary abnormalities in young cattle. This tool is particularly useful for detecting the lung consolidation associated with bronchopneumonia following bacterial infection of the lower airway. The aim of this review is to discuss on-farm LUS techniques, the contributions of LUS to bovine respiratory disease research, and potential applications in cattle practice. Lung ultrasonography studies consistently demonstrate associations between lung consolidation and negative economic outcomes, including (among others): reduced growth, lower future carcass weights in veal calves, premature culling, and lower future milk production in dairy cattle. Within the context of subclinical pneumonia (presence of lung lesions in the absence of abnormal clinical signs), the dynamics of respiratory tract infections and the presence of specific risk factors could be better characterized. Given its higher diagnostic sensitivity (ranging from 66% to 94%) and specificity (from 66% to 100%) for detecting calves affected with lung disease, LUS is a better reference test for randomized clinical trials evaluating therapy and vaccine efficacy compared with clinical scoring. In the handful of vaccination studies available, LUS results were significantly different between experimental groups despite no effect on clinical scores, demonstrating the added value of using this ancillary test as an outcome. On-farm applications of LUS include pneumonia detection for the purposes of monitoring patterns of disease, evaluating of clinical detection accuracy, initiating treatment, evaluating treatment efficacy, cure definition or determining duration of treatment, conducting pre-purchase examinations, and making culling decisions. Two LUS scoring systems that are based on quantifying lung consolidation, and therefore are best for characterizing bronchopneumonia, are commonly in use. Currently, there is no LUS scoring system for quantifying the severity of diffuse airway injury from viral infections or interstitial disease in dairy or veal calves. There is a need for a core outcome set for studies on respiratory-focused research that include LUS parameters as case definitions or treatment responses, in addition to key performance indicators (production and health outcomes), in order to motivate the dairy, dairy beef, and veal calf industries toward more sustainable production. Fortunately, momentum for implementing on-farm LUS is growing, but more work needs to be done to increase producer awareness and expand veterinary, research, or technical training. Certificate programs to document well-trained and highly qualified-professionals may prove useful for promoting on-farm implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".