Study Designs and Systematic Reviews of Interventions: Building Evidence Across Study Designs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.473 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.032 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it