Systematic reviews and meta-analyses in animal health, performance, and on-farm food safety: a scoping review
Bibliographic record
Abstract
Systematic reviews and meta-analyses are used to summarize and interpret evidence for clinical decision-making in human health. The extent of the application of these methods in veterinary medicine and animal agriculture is unknown. The goal of this scoping study was to ascertain trends in the publication of systematic reviews and meta-analyses examining animal health, animal performance, and on-farm food safety. Online databases were searched for reviews published between 1993 and 2018 that focused on relevant outcomes in domestic livestock, companion animals, or wildlife species. In total 1787 titles and abstracts underwent data characterization. Dairy cattle, fish, and pigs were the most common target commodity groups. Few articles investigated both health and performance outcomes (only health: n = 418; only performance: n = 701; both health and performance: n = 103). Most of the reviews (67.6%, n = 1208/1787) described a meta-analysis but did not state in the title or abstract that a systematic review was also conducted, which is potentially problematic. Adherence to reporting guidelines is recommended for all systematic reviews and meta-analyses. For research areas with many reviews, an evidence repository is recommended. For less well-reviewed areas, additional investigation may be necessary to identify the reasons for the lack of synthesis research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.029 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads 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".