Emerging Echinococcus tapeworms: fecal PCR detection of Echinococcus multilocularis in 26 dogs from the United States and Canada (2022–2024)
Bibliographic record
Abstract
OBJECTIVE: To report quantitative PCR (qPCR) detection of Echinococcus multilocularis DNA in fecal samples from 26 dogs in the US and Canada. ANIMALS: 26 dogs with fecal samples submitted for parasite screening by qPCR. CLINICAL PRESENTATION: Dog signalment, presenting concern, preventive care, and outcomes were obtained from the primary veterinarian via email or telephone, where available. RESULTS: Echinococcus multilocularis was detected in 26 of 2,333,797 dog fecal samples by reference laboratory fecal qPCR surveillance between March 2022 and July 2024. Seventeen E multilocularis-detected samples were sequenced as European haplotypes (E3/E4). Taenia-type eggs were identified by zinc sulfate centrifugal flotation in 8 of 17 samples (47%). Dogs were from the US (n = 16) and Canada (10). Ten dogs had gastrointestinal signs (diarrhea) reported on initial presentation. Clinical history revealed that some dogs were receiving a monthly antiparasitic preventive in the 6-month period prior to sampling (n = 10) and had regular wildlife (rodent) exposure (13). Twenty-five dogs were subsequently confirmed to have received treatment with praziquantel for detected E multilocularis, and 25 of these dogs were qPCR negative 3 to 5 weeks after treatment. CLINICAL RELEVANCE: Veterinary awareness of endemic risk regions for E multilocularis and its emergence in novel areas (Colorado, Nevada, Wyoming, Montana, Illinois, Washington, Idaho, Kansas, and Oregon) are key for One Health. Dogs can serve as sentinels for Echinococcus tapeworm risk, and detection of E multilocularis tapeworms in dogs through routine qPCR fecal screening can alert clinicians to zoonotic concern and common environmental exposure risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".