Characterization and optimization of visual pen checking criteria to improve bovine respiratory disease treatment outcomes in newly arrived feedlot cattle
Bibliographic record
Abstract
An observational study took place during the fall and winter months of 2021 and 2022, at commercial feedlots in southern Alberta and northern Saskatchewan (n = 5). The purpose of the study was to identify the clinical signs of bovine respiratory disease (BRD). All calves identified for BRD treatment (n = 163) had rectal temperature (RT), blood lactate (BL), and computer-aided lung auscultation (CALA) score measured at chute-side. The thresholds for case definitions were RT ≥ 40°C, BL concentration ≥ 4 mmol/L and CALA score ≥ 2. Nose secretions (Odds Ratio (OR) = 2.43, P = 0.02) and abnormal ear position (OR = 2.11, P = 0.22) were positively associated when the BRD case definition was based on RT. A BRD case definition that combined RT and BL was positively associated with ear position (OR = 5.65, P = 0.11), nose secretions (OR = 3.02, P = 0.04), and lack of stretching (OR = 2.53, P = 0.68). A case definition based on RT, BL and LA scores was positively associated with nose secretions (OR = 7.70, P = 0.01). Treatment outcomes were split into number of BRD treatments based on after the fact health records. Cattle with 2 treatments were associated with abnormal respiration (OR = 1.99, P = 0.35) and ear position (OR = 1.89, P = 0.28). Three treatments were associated with abnormal respiration (OR = 2.77, P = 0.16) and mouth secretions (OR = 2.59, P = 0.12). Chronic cases of 4 or more treatments were associated with abnormal respiration (OR = 5.86, P = 0.09) and nose secretions (OR = 2.44, P = 0.41). Lastly, BRD death cases were associated with flat tail (OR=3.58, P = 0.08) and mouth secretions (OR = 2.52, P = 0.23). To follow, a survey was distributed throughout western Canada, to find commonalities between different cohorts of pen riders. The questions were organized in three sections: background of respondent, methodology to diagnose BRD, and videos of animals with varying severities of BRD. Most of the respondents were male (67%), 18-30 years old (33%), had an average of 11 years of experience pen checking (Median = 8 yrs, SD = 10 yrs), and received training only at the beginning of career by a colleague (79%). A total of 65% of the pen checkers considered laboured breathing/altered respiration the most important clinical sign for BRD diagnosis, followed by slow moving (52%), body posture/head carriage (51%) and isolation from the herd (40%). The video-based questions consisted of eight 10-sec clips of calves recorded in field conditions while evaluated for BRD. Seven clips corresponded to BRD cases with different symptomatology, and one of the clips was from a control animal (no BRD symptoms). To assess the pen checkers diagnostic accuracy, 5 out of the 8 videos were used to create a score for each respondent depending on whether the pen rider was able to successfully identify BRD symptoms. Out of the 68 respondents that assessed all 5 videos, 12 of them (18%) responded correctly, while 24 (35%) responded to 4 out of 5 videos correctly. Respondents with the greater scores had 0-10 years of experience and were 18-30 years old. The research surrounding the art of pen riding should be continued as it offers an inexpensive, and practical method to prevent BRD in feedlots. Much is still unknown about the inner workings of pen riding and more research and observation is required.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".