Introduction to Systematic Reviews in Animal Agriculture and Veterinary Medicine
Bibliographic record
Abstract
This article is the first in a series of six articles related to systematic reviews in animal agriculture and veterinary medicine. In this article, we overview the methodology of systematic reviews and provide a discussion of their use. Systematic reviews differ qualitatively from traditional reviews by explicitly defining a specific review question, employing methods to reduce bias in the selection and inclusion of studies that address the review question (including a systematic and specified search strategy, and selection of studies based on explicit eligibility criteria), an assessment of the risk of bias for included studies and objectively summarizing the results qualitatively or quantitatively (i.e. via meta-analysis). Systematic reviews have been widely used to address human healthcare questions and are increasingly being used in veterinary medicine. Systematic reviews can provide veterinarians and other decision-makers with a scientifically defensible summary of the current state of knowledge on a topic without the need for the end-user to read the vast amount of primary research related to that topic.
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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 | 0.040 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".