Considerations and guidance in designing equity-relevant clinical trials
Bibliographic record
Abstract
Health research has documented disparities in health and health outcomes within and between populations. When these disparities are unfair and avoidable they may be referred to as health inequities. Few trials attend to factors related to health inequities, and there is limited understanding about how to build consideration of health inequities into trials. Due consideration of health inequities is important to inform the design, conduct and reporting of trials so that research can build evidence to more effectively address health inequities and importantly, ensure that inequities are not aggravated. In this paper, we discuss approaches to integrating health equity-considerations in randomized trials by using the PROGRESS Plus framework (Place of residence, Race/ethnicity/culture/language, Occupation, Gender, Religion, Education, Socio-economic status, Social capital and "Plus" that includes other context specific factors) and cover: (i) formulation of research questions, (ii) two specific scenarios relevant to trials about health equity and (iii) describe how the PROGRESS Plus characteristics may influence trial design, conduct and analyses. This guidance is intended to support trialists designing equity-relevant trials and lead to better design, conduct, analyses and reporting, by addressing two main issues: how to avoid aggravating inequity among research participants and how to produce information that is useful to decision-makers who are concerned with health inequities.
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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 | 0.050 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads 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".