Invited review: Recommendations for reporting intervention studies on reproductive performance in dairy cattle: Improving design, analysis, and interpretation of research on reproduction
Bibliographic record
Abstract
Abundant evidence from the medical, veterinary, and animal science literature demonstrates that there is substantial room for improvement of the clarity, completeness, and accuracy of reporting of intervention studies. More rigorous reporting guidelines are needed to improve the quality of data available for use in comparisons of outcomes (or meta-analyses) of multiple studies. Because of the diversity of factors that affect reproduction and the complexity of interactions between these, a systematic approach is required to design, conduct, and analyze basic and applied studies of dairy cattle reproduction. Greater consistency, clarity, completeness, and correctness of design and reporting will improve the value of each report and allow for greater depth of evaluation in meta-analyses. Each of these benefits will improve understanding and application of current knowledge and better identify questions that require additional modeling or primary research. The proposed guidelines and checklist will aid in the design, conduct, analysis, and reporting of intervention studies. We propose an adaptation of the REFLECT (Reporting Guidelines for Randomized Controlled Trials for Livestock and Food Safety) statement to provide guidelines and a checklist specific to reporting intervention studies in dairy cattle reproduction. Furthermore, we provide recommendations that will assist investigators to produce studies with greater internal and external validity that can more often be included in systematic reviews and global meta-analyses. Such studies will also assist the development of models to describe the physiology of reproduction.
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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 | 0.182 | 0.438 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.014 | 0.007 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".