Planning experiments: Updated guidance on experimental design and analysis and their reporting III
Bibliographic record
Abstract
Scientists who plan to publish in British Journal of Pharmacology (BJP) must read this article before undertaking a study. This editorial provides guidance for the design of experiments. We have published previously two guidance documents on experimental design and analysis (Curtis et al., 2015; Curtis et al., 2018). This update clarifies and simplifies the requirements on design and analysis for BJP manuscripts. This editorial also details updated requirements following an audit and discussion on best practice by the BJP editorial board. Explanations for the requirements are provided in the previous articles. Here, we address new issues that have arisen in the course of handling manuscripts and emphasise three aspects of design that continue to present the greatest challenge to authors: randomisation, blinded analysis and balance of group sizes.
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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 | 0.388 | 0.646 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.038 | 0.044 |
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".