Journal Initiatives to Enhance Preclinical Research: Analyses of <i>Stroke, Nature Medicine, Science Translational Medicine</i>
Bibliographic record
Abstract
Background and Purpose— Preclinical research using animals often informs clinical trials. However, its value is dependent on its scientific validity and reproducibility, which are, in turn, dependent on rigorous study design and reporting. In 2011, Stroke introduced a Basic Science Checklist to enhance the reporting and methodology of its preclinical studies. Except for Nature and Science journals, few others have implemented similar initiatives. We sought to estimate the impact of these journal interventions on the quality of their published reports. Methods— All articles published in Stroke , Nature Medicine , and Science Translational Medicine over 9 to 18 years and in 2 control journals without analogous interventions over a corresponding 11.5 years were reviewed to identify reports of experiments in nonhuman mammals with proposed clinical relevance. The effect of journal interventions on the reporting and use of key study design elements was estimated via interrupted time-series analyses. Results— Of 33 009 articles screened, 4162 studies met inclusion criteria. In the 3.5 to 12 years preceding each journal’s intervention, the proportions of studies reporting and using key study design elements were stable except for blinding in Stroke and randomization in Science Translational Medicine , which were both increasing. Post-intervention, abrupt and often marked increases were seen in the reporting of randomization status (level change: +17% to +44%, P ≤0.005), blinding (level change: +20% to +40%, P ≤0.008), and sample size estimation (level change: 0% to +40%, P ≤0.002 in 2 journals). Significant but more modest improvements in the use of these study design elements were also observed. These improvements were not seen in control journals. Conclusions— Journal interventions such as Stroke ’s author submission checklist can meaningfully improve the quality of published preclinical research and should be considered to enhance study transparency and design. However, such interventions are alone insufficient to fully address widespread shortcomings in preclinical research practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".