Association of lung lesions measured by thoracic ultrasonography at first diagnosis of bronchopneumonia with relapse rate and growth performance in feedlot cattle
Bibliographic record
Abstract
Abstract Background Severity of lung lesions quantified by thoracic ultrasonography (TUS) at time of bronchopneumonia (BP) diagnosis predicted death among steers not treated for this condition. Further research is needed to confirm that lung lesions detected by TUS can be associated with negative outcomes in cattle with BP that subsequently were treated. Objective To quantify the effects on relapse rate and average daily gain (ADG) of lung lesions detected by TUS at first BP diagnosis in feedlot cattle. Animals Prospective cohort of mixed beef-breed steers (n = 93; 243 ± 36 kg) and heifers (n = 51; 227 ± 42 kg) with BP at 4 feedlots. Methods Thoracic ultrasonography was performed by the same clinician and 16-second TUS videos were evaluated offline for maximal depth and area of lung consolidation, maximum number of comet tails, and maximal depth of pleural fluid. Individual ADG was calculated between 1 and 120 days after arrival. Effects of lesions on relapse rate and ADG were investigated using mixed regression models. Results Maximal depth of lung consolidation was associated with a higher risk of relapse (odds ratio [OR], 1.337/cm; 95% confidence interval [CI], 1.042-1.714) and lower ADG (− 34 g/cm; −64 to −4). Maximal area of lung consolidation also was associated with a higher relapse risk (OR, 1.052/cm2; 1.009-1.097) but not with ADG. Comet tails and pleural fluid were not associated with risk of relapse or ADG. Conclusions and Clinical Importance Quantifying maximal depth and area of lung consolidation by TUS at first BP diagnosis can provide useful prognostic information in feedlot cattle.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".