Evidence of improved reporting of swine vaccination trials in the post-REFLECT statement publication period
Bibliographic record
Abstract
Objectives: Describe and compare the proportion of studies reporting the method used to assign study units to treatment groups, reporting a random allocation approach, reporting 18 REFLECT items, and the proportion of studies having a low risk-of-bias assessment in swine vaccination trial studies published after the REFLECT statement, compared to studies published before. Materials and Methods: The study population was 61 studies that evaluated vaccines targeted at pathogens affecting swine health or pork safety. Two reviewers assessed the reporting of 18 of 22 REFLECT items and 5 risk-of-bias domains. Results: Authors reported the method used to allocate experimental units in 33 of 42 (79%) and 14 of 19 (74%) studies published prior to and following REFLECT, respectively. There has been a substantial shift in the reporting of allocation approaches. Before 2011, only 2 of 25 (8%) studies that reported using random allocation provided supporting evidence. This increased in studies published between 2011-2017 (4 of 6; 66%). Before 2011, 8 of 33 (24%) studies reported using systematic allocation, which increased to 43% (6 of 14 studies) between 2011-2017. There has also been an increase in the prevalence of reporting for 14 of the 18 REFLECT items. There was an increase in the number of studies reporting evidence to support true randomization to group and data that suggests few baseline imbalances. Implications: Data from this study suggests swine vaccination trial reporting improved, which may be due to researchers having more access to better quality information.
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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 | 0.685 | 0.904 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".