A systematic review and meta-analyses of the association between 4 mycoplasma species and lower respiratory tract disease in dogs
Bibliographic record
Abstract
BACKGROUND: The pathogenic role of mycoplasmas in the lower respiratory tract (LRT) of dogs is debated, because mycoplasmas can be isolated from both healthy and sick dogs. OBJECTIVES: To critically assess available data from controlled observational studies on the role of 4 mycoplasma species in LRT disease of dogs. DESIGN: Systematic review and meta-analyses. METHODS: Seven electronic databases were searched for relevant publications. Risk of bias was assessed by the Newcastle-Ottawa Scale. Meta-analyses, stratified by mycoplasmal species, were performed using a random effects Bayesian model with noninformative priors to estimate pooled odds ratios (ORs) and 95% confidence intervals (CIs) for the association between Mycoplasma cynos, Mycoplasma canis, Mycoplasma spumans, and Mycoplasma edwardii and LRT disease in dogs. RESULTS: Five studies were included from 1201 references identified. All studies dealt with M. cynos, whereas 3 dealt with the other mycoplasma species. A significant association was found between M. cynos and LRT disease (Bayesian OR, 3.60; CI, 1.31-10.29). Conversely, M. canis, M. spumans, and M. edwardii were not significantly associated with LRT signs (Bayesian OR, 1.06; CI, 0.10-14.63; Bayesian OR, 3.40; CI, 0.16-54.27; and Bayesian OR, 1.04; CI, 0.05-23.54, respectively). CONCLUSIONS AND CLINICAL IMPORTANCE: Results support a pathogenic role of M. cynos and a commensal role of M. canis and M. edwardii in LRT in dogs. Although the association was not significant based on the CI, the point estimate of the Bayesian OR was relatively high for M. spumans, making its role less clear. Mycoplasma cynos-specific polymerase chain reaction should be considered on samples from dogs with LRT.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".