Prevalence of enteric pathogens in diarrhoeic and nondiarrhoeic foals
Bibliographic record
Abstract
Background : Diarrhoea is a significant cause of morbidity and mortality in foals. However, further research is needed to understand the occurrence of pathogens in both single and coinfections among extensively raised foals, with and without diarrhoea. Hypothesis/Objectives : Our hypothesis is that foals with diarrhoea have a greater prevalence of organisms detected in coinfections. Therefore, this study investigated the major microorganisms associated with diarrhoea in foals with and without diarrhoea. Study design : A total of 200 foals (100 diarrhoeic and 100 nondiarrhoeic), up to 1 year of age, were included in this study. Methods : Faecal samples were analysed for the detection of Clostridioides difficile (bacterial culture and toxin A/B detection), Clostridium perfringens (bacterial culture and genotyping) and Salmonella spp., Rhodococcus equi , Lawsonia intracellularis , Neorickettsia risticii , Enterococcus durans , Giardia duodenalis, Cryptosporidium spp. , rotavirus A and coronavirus (real-time PCR). Results : At least one enteric agent was detected in 85% and 70% of diarrhoeic and nondiarrhoeic foals, respectively. Codetection was significantly more frequent in the diarrhoeic group (27 singly detected organisms vs. 58 codetected organisms) than in the nondiarrhoeic group (37 singly detected organisms vs. 33 codetected organisms) (P = 0.0079). Salmonella spp., C. difficile (and A/B toxin gene detection) and Cryptosporidium spp. were significantly associated with foal diarrhoea. Conclusions and clinical relevance : The detection of multiple agents in foals with diarrhoea highlights the complexity of diagnosis and the potential interaction between agents in the multifactorial aetiology of this condition.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".