Conducting Systematic Reviews of Intervention Questions I: Writing the Review Protocol, Formulating the Question and Searching the Literature
Bibliographic record
Abstract
This article is the fourth of six articles addressing systematic reviews in animal agriculture and veterinary medicine. Previous articles in the series have introduced systematic reviews, discussed study designs and hierarchies of evidence, and provided details on conducting randomized controlled trials, a common design for use in systematic reviews. This article describes development of a review protocol and the first two steps in a systematic review: formulating a review question, and searching the literature for relevant research. The emphasis is on systematic reviews of questions related to interventions. The review protocol is developed prior to conducting the review and specifies the plan for the conduct of the review, identifies the roles and responsibilities of the review team and provides structured definitions related to the review question. For intervention questions, the review question should be defined by the PICO components: population, intervention, comparison and outcome(s). The literature search is designed to identify all potentially relevant original research that may address the question. Search terms related to some or all of the PICO components are entered into literature databases, and searches for unpublished literature also are conducted. All steps of the literature search are documented to provide transparent reporting of the process.
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 | 0.413 | 0.570 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.018 | 0.017 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.054 | 0.021 |
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".