Challenges and opportunities for the adoption of molecular diagnostics for anthelmintic resistance
Bibliographic record
Abstract
Anthelmintic resistance is a significant threat to livestock production systems worldwide and is emerging as an important issue in companion animal parasite management. It is also an emerging concern for the control of human soil-transmitted helminths and filaria. An important aspect of managing anthelmintic resistance is the ability to utilise diagnostic tests to detect its emergence at an early stage. In host-parasite systems where resistance is already widespread, diagnostics have a potentially important role in determining those drugs that remain the most effective. The development of molecular diagnostics for anthelmintic resistance is one focus of the Consortium for Anthelmintic Resistance and Susceptibility (CARS) group. The present paper reflects discussions of this issue that occurred at the most recent meeting of the group in Wisconsin, USA, in July 2019. We compare molecular resistance diagnostics with in vivo and in vitro phenotypic methods, and highlight the advantages and disadvantages of each. We assess whether our knowledge on the identity of molecular markers for resistance towards the different drug classes is sufficient to provide some expectation that molecular tests for field use may be available in the short-to-medium term. We describe some practical aspects of such tests and how our current capabilities compare to the requirements of an 'ideal' test. Finally, we describe examples of drug class/parasite species interactions that provide the best opportunity for commercial use of molecular tests in the near future. We argue that while such prototype tests may not satisfy the requirements of an 'ideal' test, their potential to provide significant advances over currently-used phenotypic methods warrants their development as field diagnostics.
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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 | 0.114 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".