Risk factors for canine infectious respiratory disease complex and the pathogens associated with the disease.
Bibliographic record
Abstract
Background: Canine infectious respiratory disease complex (CIRDC) is a common respiratory condition typically associated with high-density populations. Objectives: The objectives of this study were to determine the most common pathogens involved in CIRDC and to identify risk factors (pathogens, environmental exposures) associated with the diagnosis. Animals and procedure: A prospective, multi-clinic, case-control study was conducted in Canada from April 2017 to May 2018. A total of 110 dogs (74 cases, 36 controls) were enrolled by participating veterinary clinics. Pathogens were detected using a respiratory PCR panel. Results: , and canine respiratory coronavirus (CRCoV) were detected in cases only. According to 2 logistic regression models, detection of CPIV (OR: 14.42; 95% CI: 2.24 to ∞) and CRCoV (OR: 8.64; 95% CI: 1.26 to ∞) were associated with CIRDC disease status. In another model, exposures to multiple-dog gatherings also increased the odds (OR: 3.39; 95% CI: 1.26 to 9.81) of CIRDC diagnosis. Conclusions: were important contributors to CIRDC cases. Detection of CPIV and CRCoV and exposure to areas of dog gatherings were identified as having a role in disease status when evaluated statistically, under the conditions of this study.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".