The REFLECT Statement: Methods and Processes of Creating Reporting Guidelines for Randomized Controlled Trials for Livestock and Food Safety
Bibliographic record
Abstract
Abstract The conduct of randomized controlled trials in livestock with production, health, and food-safety outcomes presents unique challenges that might not be adequately reported in trial reports. The objective of this project was to modify the CONSORT (Consolidated Standards of Reporting Trials) statement to reflect the unique aspects of reporting these livestock trials. A 2-day consensus meeting was held on November 18–19, 2008 in Chicago, IL, to achieve the objective. Before the meeting, a Web-based survey was conducted to identify issues for discussion. The 24 attendees were biostatisticians, epidemiologists, food-safety researchers, livestock production specialists, journal editors, assistant editors, and associate editors. Before the meeting, the attendees completed a Web-based survey indicating which CONSORT statement items would need to be modified to address unique issues for livestock trials. The consensus meeting resulted in the production of the REFLECT (Reporting Guidelines for Randomized Control Trials) statement for livestock and food safety and 22-item checklist. Fourteen items were modified from the CONSORT checklist, and an additional subitem was proposed to address challenge trials. The REFLECT statement proposes new terminology, more consistent with common usage in livestock production, to describe study subjects. Evidence was not always available to support modification to or inclusion of an item. The use of the REFLECT statement, which addresses issues unique to livestock trials, should improve the quality of reporting and design for trials reporting production, health, and food-safety outcomes.
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 | 0.722 | 0.877 |
| Meta-epidemiology (narrow) | 0.008 | 0.011 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.035 | 0.030 |
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".