Invited review: Maximizing value and minimizing waste in clinical trial research in dairy cattle: Selecting interventions and outcomes to build an evidence base
Bibliographic record
Abstract
Clinical trials are a valuable study design for evaluating interventions when it is ethical and feasible for investigators to randomly allocate study animals to intervention groups. Researchers may choose to evaluate the comparative efficacy of intervention groups for their effect on outcomes that are relevant to the specific objectives of their trial. However, the results across multiple trials on the same intervention and with the same outcome should be considered when making decisions on whether to use an intervention, because the results of a single trial are subject to sampling error and do not reflect all biological variability. The objective of this review was to provide an overview of important concepts when selecting intervention groups and outcomes within a randomized controlled trial, and when building a body of evidence for intervention efficacy across multiple trials. Empirical evidence is presented to highlight that integrating and interpreting the efficacy of an intervention across trials is hindered by a lack of replication of interventions across trials. Inconsistency in the outcomes and their measurement among trials also limits the ability to build a body of evidence for the efficacy of interventions. The development of core outcome sets for specific topic areas in dairy science, updated as necessary, may improve consistency across trials and aid in the development of a body of evidence for evidence-based decision-making.
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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 | 0.025 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".