Protocol for a systematic scoping review of reasons given to justify the performance of randomised controlled trials
Bibliographic record
Abstract
INTRODUCTION: Randomised controlled trials (RCTs) are widely viewed to generate the most reliable medical knowledge. However, RCTs are not always scientifically necessary and therefore not always ethical. Unfortunately, it is not clear when an RCT is not necessary or how this should be established. This study seeks to systematically catalogue justifications offered throughout the medical and ethics literature for performing randomisation within clinical trials. METHODS AND ANALYSIS: We will systematically search electronic databases of the medical literature including MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Clinical Trials Register, Web of Science Proceedings, ClinicalTrials.gov; databases of philosophical literature including Philosopher's Index, Phil Papers, JSTOR, Periodicals Archive Online, Project MUSE, National Reference Centre for Bioethics; the library catalogue at the University of Ottawa; bibliographies of retrieved papers; and the grey literature. We will also pursue suggestions from experts in the fields of medical ethics, philosophy and clinical trial methodology. Article screening, selection and data extraction will be performed by two independent reviewers based on prespecified inclusion/exclusion criteria. A third reviewer will be consulted to resolve any discrepancies. We will then extract the reasons given to justify randomisation using methodology established to extract data in a defensible, systematic manner. We will track the reasons given, their frequency of use and changes over time. Finally, using grounded theory, we will combine the reasons into broader themes. These themes will form the foundation of our subsequent analysis from qualitative and quantitative perspectives. This review will map existing arguments that clinicians, ethicists and philosophers use to ethically justify randomisation in clinical trials. ETHICS AND DISSEMINATION: No research ethics board approval is necessary because we are not examining patient-level data. This protocol complies with the reported guidance for conducting systematic scoping reviews. The findings of this paper will be disseminated via presentations and academic publication. In a subsequent phase of this research, we hope to engage with stakeholders and translate any recommendations derived from our findings into operational guidelines.
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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 | 0.280 | 0.402 |
| Meta-epidemiology (narrow) | 0.009 | 0.010 |
| Meta-epidemiology (broad) | 0.021 | 0.019 |
| Bibliometrics | 0.029 | 0.027 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.082 | 0.029 |
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".