The use of lung biopsy to determine early lung pathology and its association with health and production outcomes in feedlot steers.
Bibliographic record
Abstract
The objectives of this study were to determine if percutaneous lung biopsy can be used to characterize early pathologic changes in bovine lung associated with bovine respiratory disease (BRD), to determine if specific infectious respiratory pathogens can be identified in association with these changes, and to determine whether pulmonary pathology at arrival and at the time of initial diagnosis are associated with health and production outcomes. One hundred auction-market derived crossbred steer calves from a commercial feedlot in southern Alberta were included in this study. A percutaneous lung biopsy technique was used to obtain lung samples from the right middle lung. Steers were sampled 295 times yielding 283 samples with 210 (74%) containing lung tissue. Overall, histopathological changes were observed in 20 (9.5%) of lung biopsy samples. There were too few samples with pathology to reveal an association between lung pathology and subsequent health events. In general, percutaneous lung biopsy can be done safely on feedlot steers in a commercial feedlot setting with few clinical side effects. This technique did not prove useful as a diagnostic tool or prognostic indicator for early BRD. However, it may be useful for the diagnosis of BRD in targeted populations of commercial feedlot steers.
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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 | 0.002 | 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) | 0.000 | 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".