Conducting Systematic Reviews of Intervention Questions <scp>III</scp>: Synthesizing Data from Intervention Studies Using Meta‐Analysis
Bibliographic record
Abstract
This article is the sixth in a series of six articles describing systematic reviews of interventions in animal agriculture and veterinary medicine. The first article provided an overview of systematic reviews, followed by an article on building evidence across study designs, and an article describing criteria for validity in randomized controlled trials. The fourth article in this series overviewed the initial steps in conducting a systematic review: development of a review protocol, identification of the structured question to be addressed and conducting a comprehensive literature search to identify potentially relevant research to address the review question. The fifth article introduced relevance screening of literature to identify and include research that is relevant to the review question, the use of standardized checklists and procedures to assess the risk of bias in the relevant research, data extraction from primary research studies and summarizing the results of the body of research identified. Many systematic reviews of interventions aim to use a quantitative method to combine the results of multiple studies and provide a more precise estimate of the effect of the intervention on the outcome, that is, a summary effect measure. The objective of this article was to describe general approaches that are available for quantitative synthesis of data. Specific details of all meta-analysis statistical approaches are beyond the capacity of 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.
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 | 0.459 | 0.721 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.035 | 0.032 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".