Quality of Reporting in Clinical Trials of Preharvest Food Safety Interventions and Associations with Treatment Effect
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are the gold standard for evaluating treatment efficacy. Therefore, it is important that RCTs are conducted with methodological rigor to prevent biased results and report results in a manner that allows the reader to evaluate internal and external validity. Most human health journals now require manuscripts to meet the Consolidated Standards of Reporting Trials (CONSORT) criteria for reporting of RCTs. Our objective was to evaluate preharvest food safety trials using a modification of the CONSORT criteria to assess methodological quality and completeness of reporting, and to investigate associations between reporting and treatment effects. One hundred randomly selected trials were evaluated using a modified CONSORT statement. The majority of the selected trials (84%) used a deliberate disease challenge, with the remainder representing natural pathogen exposure. There were widespread deficiencies in the reporting of many trial features. Randomization, double blinding, and the number of subjects lost to follow-up were reported in only 46%, 0%, and 43% of trials, respectively. The inclusion criteria for study subjects were only described in 16% of trials, and the number of animals housed together was only stated in 52% of the trials. Although 91 trials had more than one outcome, no trials specified the primary outcome of interest. There were significant bivariable associations between the proportion of positive treatment effects and failure to report the number of subjects lost to follow-up, the number of animals housed together in a group, the level of treatment allocation, and possible study limitations. The results suggest that there are substantive deficiencies in reporting of preharvest food safety trials, and that these deficiencies may be associated with biased treatment effects. The creation and adoption of standards for reporting in preharvest food safety trials will help to ensure the inclusion of important trial details in all publications.
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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 | 0.726 | 0.889 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.015 | 0.024 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".