World Association for the Advancement of Veterinary Parasitology (W.A.A.V.P.) guideline for diagnosing anthelmintic resistance using the faecal egg count reduction test in ruminants, horses and swine
Bibliographic record
Abstract
The faecal egg count reduction test (FECRT) remains the method of choice for establishing the efficacy of anthelmintic compounds in the field, including the diagnosis of anthelmintic resistance. We present a guideline for improving the standardization and performance of the FECRT that has four sections. In the first section, we address the major issues relevant to experimental design, choice of faecal egg count (FEC) method, statistical analysis, and interpretation of the FECRT results. In the second section, we make a series of general recommendations that are applicable across all animals addressed in this guideline. In the third section, we provide separate guidance details for cattle, small ruminants (sheep and goats), horses and pigs to address the issues that are specific to the different animal types. Finally, we provide overviews of the specific details required to conduct an FECRT for each of the different host species. To address the issues of statistical power vs. practicality, we also provide two separate options for each animal species; (i) a version designed to detect small changes in efficacy that is intended for use in scientific studies, and (ii) a less resource-intensive version intended for routine use by veterinarians and livestock owners to detect larger changes in efficacy. Compared to the previous FECRT recommendations, four important differences are noted. First, it is now generally recommended to perform the FECRT based on pre- and post-treatment FEC of the same animals (paired study design), rather than on post-treatment FEC of both treated and untreated (control) animals (unpaired study design). Second, instead of requiring a minimum mean FEC (expressed in eggs per gram (EPG)) of the group to be tested, the new requirement is for a minimum total number of eggs to be counted under the microscope (cumulative number of eggs counted before the application of a conversion factor). Third, we provide flexibility in the required size of the treatment group by presenting three separate options that depend on the (expected) number of eggs counted. Finally, these guidelines address all major livestock species, and the thresholds for defining reduced efficacy are adapted and aligned to host species, anthelmintic drug and parasite species. In conclusion, these new guidelines provide improved methodology and standardization of the FECRT for all major livestock species.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.034 |
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".