Towards Patient-Centered Clinical Trial Designs
Bibliographic record
Abstract
Rationale, aims and objectives: Evidence shows a trend towards low enrollment in randomized clinical trials (RCTs), which negatively affect validity of conclusions. Low enrollment is associated with different factors, but has recently been attributed to an increasing proportion of patients expressing concerns about randomization. In this paper, we summarize the evidence on reasons for non-enrollment, and we propose preference-based and shared decision-making as alternative methods for allocating patients to treatments in effectiveness and comparative effectiveness trials.Methods: This paper is a narrative review of available literature.Results: Converging findings of quantitative and qualitative studies revealed three interrelated and frequently mentioned reasons for declining enrollment in RCTs: 1) concerns about randomization related to the lack of understanding of equipoise, lack of appreciation of the scientific merits of randomization, and unfavorable perceptions of randomization as not reflecting methods of treatment selection used in practice; 2) preferences for treatments under evaluation, which contribute to unwillingness to be randomized; and 3) desires for involvement in treatment decision-making, which are not respected with randomization.Conclusions: Alternative methods for treatment allocation are needed to make effectiveness and comparative effectiveness trials attractive to patients. Preference-based and shared decision-making are viable methods that respectively represent the informed choice and the collaborative choice styles of treatment selection commonly used in practice. The extent to which these two methods of treatment allocation enhance enrollment should be further investigated.
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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 | 0.693 | 0.682 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".