Conducting Systematic Reviews of Intervention Questions <scp>II</scp>: Relevance Screening, Data Extraction, Assessing Risk of Bias, Presenting the Results and Interpreting the Findings
Bibliographic record
Abstract
This is the fifth in a series of six articles describing systematic reviews in animal agriculture and veterinary medicine. The previous articles in this series overviewed the development of a review protocol and the initial steps in conducting a systematic review: identification of a structured question to be answered and conducting a comprehensive literature search to find potentially relevant original research to address the review question. This article describes relevance screening of literature identified in the search to determine which of the original research articles are relevant to the review question, data extraction from primary research studies, the use of standardized procedures to assess the risk of bias in the relevant research studies, presenting the results of the body of research identified and interpreting these results.
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 | 0.408 | 0.654 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.017 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".