Best practices for recruitment in veterinary clinical trials
Bibliographic record
Abstract
A successful clinical trial requires participants, but many factors can impede effective study recruitment. To better recruit for quality veterinary clinical trials in client-owned animals that lead to improved evidence-based patient care and outcomes, there is a collective need to share and implement current best practices for recruitment strategies. These strategies should utilize a holistic view of recruitment, encompassing study design and logistics, representative participation, incentives, personnel resources, advertising, and participant retention. Although human clinical trial data and resources can provide guidance, effort also needs to be put into evaluating current practices and opportunities for process improvement that are specific to the conduct of veterinary clinical trials. Considering the power of pets as naturally occurring models of disease and as sentinels, improved conduct of veterinary clinical research has the potential to inform human health outcomes. Continued development of collaborations surrounding best practices and training opportunities in veterinary clinical research will improve the impact of veterinary clinical trials teams, while also promoting workforce development and alternate career paths for veterinary professionals.
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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 | 0.390 | 0.506 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".