Scoping Reviews, Systematic Reviews, and Meta-Analysis: Applications in Veterinary Medicine
Bibliographic record
Abstract
Evidence-based decision making is a hallmark of effective veterinary clinical practice. Scoping reviews, systematic reviews, and meta-analyses all are methods intended to provide transparent and replicable ways of summarizing a body of research to address an important clinical or public health issue. As these methods increasingly are being used by researchers and read by practitioners, it is important to understand the distinction between these techniques and to understand what research questions they can, and cannot, address. This review provides an overview of scoping reviews, systematic reviews, and meta-analysis, including a discussion of the method and uses. A sample dataset and coding to conduct a simple meta-analysis in the statistical program R also are provided. Scoping reviews are a descriptive approach, designed to chart the literature around a particular topic. The approach involves an extensive literature search, following by a structured mapping, or charting, of the literature. The results of scoping reviews can help to inform future research by identifying gaps in the existing literature and also can be used to identify areas where there may be a sufficient depth of literature to warrant a systematic review. Systematic reviews are intended to address a specific question by identifying and summarizing all of the available research that has addressed the review question. Questions types that can be addressed by a systematic review include prevalence/incidence questions, and questions related to etiology, intervention efficacy, and diagnostic test accuracy. The systematic review process follows structured steps with multiple reviewers working in parallel to reduce the potential for bias. An extensive literature search is undertaken and, for each relevant study identified by the search, a formal extraction of data, including the effect size, and assessment of the risk of bias is performed. The results from multiple studies can be combined using meta-analysis. Meta-analysis provides a summary effect size, and allows heterogeneity of effect among studies to be quantified and explored. These evidence synthesis approaches can provide scientific input to evidence-based clinical decision-making for veterinarians and regulatory bodies, and also can be useful for identifying gaps in the literature to enhance the efficiency of future research in a topic area.
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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 | 0.329 | 0.661 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.020 |
| Bibliometrics | 0.085 | 0.094 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".