Parasitological Procedures, Skills, and Areas of Knowledge Used by Small-Animal Practitioners in North America
Bibliographic record
Abstract
We designed a study to assess veterinarians' competency regarding parasitological procedures, skills, and areas of knowledge currently used in small-animal practice. The outcome will help us refine our curriculum on the basis of the parasitological working knowledge and skill sets that small-animal practitioners are using today. A questionnaire was developed and sent to small-animal practitioners. Their responses provided general information on practice characteristics, parasitological procedures used, and client education. Parasitological procedures included those to diagnose helminths, protozoa, and ectoparasites. We focused on three questions: "Do you perform or request this procedure?" "Where is this procedure performed?" and "What is your frequency?" The respondents were 478 small-animal practitioners. We performed descriptive analyses of practice characteristics along with bivariate and multivariate analyses. These analyses revealed the clinical competence of parasitological diagnoses performed or requested by small-animal practitioners. The results showed that more involved or time-consuming methods such as fecal flotation using centrifugation and the Baermann test are more often sent to a diagnostic laboratory and are requested more often by veterinarians in larger practices (i.e., those that employ more veterinarians). The outcomes also suggest that the main diagnostician may not fully understand the tests available at the diagnostic laboratory, which has an impact on decision making for management, treatment, and prevention of parasites and ultimately client education. In addition, small-animal practitioners who have been in practice longer and practices that employ five veterinarians or fewer (i.e., smaller practices) offer more client education.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".