Use and misuse of meta-analysis in Animal Science
Bibliographic record
Abstract
In animal sciences the number of published meta-analyses increases with a rate of 15% per year highlighting an actual success. This current review focuses on the good practices and traps in the conduct of meta-analyses in animal sciences, nutrition in particular. The implementation of a meta-analysis is done in several phases after the definition of the study objectives. Clearly described rules and principles of traceability should be applied as soon as the publications are collected and selected with a target of meta-analysis. Then, the coding phase is essential because it determines the quality of the graphical and statistical interpretations of the database. Following this step, the study of the levels of orthogonality of factors and of the degree of data balance of the meta-design represents an essential phase to ensure the validity of statistical processing. The issue of the choice between fixed or random effect to study and to control heterogeneity is also discussed. It appears on the basis of several examples that this choice does not generally have any influence on the conclusions of a meta-analysis when the number of experiments is sufficient. Finally, reflections are presented on the potential interest of meta-analyses in the context of systemic approaches as well as to improve mechanistic modelling work.
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 | 0.490 | 0.686 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".