Health outcomes following antimicrobial treatment of septic arthritis in Canadian feedlot calves.
Bibliographic record
Abstract
Objective: To assess the clinical efficacy of treating septic arthritis (SA) with 3 different antimicrobials: oxytetracycline, florfenicol, and tulathromycin. Animal: The study population comprised fall-placed steer and heifer calves at 4 commercial western Canadian feedlots. All calves received tulathromycin at induction for bovine respiratory disease (BRD). Procedure: , SA) and SA alone. Additional outcomes were early shipment for salvage slaughter (railer) and the sum of infectious mortality events and railer events (total fallout). Calves were followed for 90 d post-allocation. Results: > 0.05). Conclusion: This clinical field trial identified no health outcome differences when comparing florfenicol, oxytetracycline, and tulathromycin for the 1st treatment of SA in feedlot calves. Clinical relevance: Practitioners can use these results when creating treatment protocols for SA cases. Since results indicated similar health outcomes among the 3 antimicrobials, the lowest-priced antimicrobial may be the most cost-effective option. Perhaps antimicrobial treatment does not affect SA outcomes, but this could not be determined as negative controls were not included in this study.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".