Systematic reviews of animal studies – Report of an international symposium
Bibliographic record
Abstract
OBJECTIVE: The Symposium on Animal Systematic Reviews held 24 May 2022, sought to bring organisations working on animal literature searching and systematic reviews together into the same virtual space for introductions and discussion. BACKGROUND: Groups working on animal research synthesis are often siloed into preclinical, veterinary, and One Health settings. This symposium sought to define commonalities and differences in methodologies, resources, and philosophies and to discuss future needs. METHODS: The 3-hour virtual symposium for veterinarians, researchers, and information specialists began with introductions by panelists from organisations involved in searching the literature for animal studies and conducting systematic reviews. This was followed by a panel discussion and question and answer period. RESULTS: Panelists identified a need to ensure planning and accurate description of primary animal studies as a precursor to quality systematic reviews. They acknowledged and discussed differences in evidence synthesis expectations and tools based on the type of review, the types of studies available on the topic, and the focus on preclinical, veterinary, or One Health topics. CONCLUSION: The need to increase the speed and quality of evidence reviews, and to automate updates, requires investing in the development of both skilled teams and platforms. The symposium provided a chance to identify existing resources, define challenges, and note gaps unique to systematic reviews of animal studies. APPLICATION: This symposium acts as a baseline for ongoing discussions centred on improving the culture and pipeline for evidence syntheses of animal studies that inform decision-making.
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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 | 0.250 | 0.173 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".