Study Designs and Systematic Reviews of Interventions: Building Evidence Across Study Designs
Bibliographic record
Abstract
This article is the second article in a series of six focusing on systematic reviews in animal agriculture and veterinary medicine. This article addresses the strengths and limitations of study designs commonly used in animal agriculture and veterinary research to assess interventions (preventive or therapeutic treatments) and discusses the appropriateness of their use in systematic reviews of interventions. Different study designs provide different evidentiary value for addressing questions about the efficacy of interventions. Experimental study designs range from in vivo proof of concept experiments to randomized controlled trials (RCTs) under real-world conditions. The key characteristic of experimental design in intervention studies is that the investigator controls the allocation of individuals or groups to different intervention strategies. The RCT is considered the gold standard for evaluating the efficacy of interventions and, if there are well-executed RCTs available for inclusion in a systematic review, that review may be restricted to only this design. In some instances, RCTs may not be feasible or ethical to perform, and there are fewer RCTs published in the veterinary literature compared to the human healthcare literature. Therefore, observational study designs, where the investigator does not control intervention allocation, may provide the only available evidence of intervention efficacy. While observational studies tend to be relevant to real-world use of an intervention, they are more prone to bias. Human healthcare researchers use a pyramid of evidence diagram to describe the evidentiary value of different study designs for assessing interventions. Modifications for veterinary medicine are presented in this article.
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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 | 0.432 | 0.734 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.020 |
| Bibliometrics | 0.055 | 0.036 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".