Seek and Ye Shall Not Find (Yet): Searching Clinical Trial Registries for Trials Designed With Patients—A Call to Action
Bibliographic record
Abstract
Unlabelled: Clinical trial registries were designed to help patients search for potentially suitable clinical trials. When our family faced another serious cancer diagnosis, we searched multiple international clinical trial registries. Despite increasing evidence that trials designed with patients can be better for trial participants (eg, they can have more relevant outcome measures and fewer burdens), it is currently impossible to search registries for these specific types of trials. In this Patient Perspective article, we make the first "call to action" for clinical trial registries to include (1) a filter that allows for efficient searching for clinical trials designed with patients and (2) structured information, in plain language, on how patients were involved. We propose how these two innovations could help reduce barriers to clinical trial participation. We also highlight how new regulatory and ethical guidelines are encouraging patient involvement in trial design, and we identify the benefits to many of doing so. Given the pressing need to improve clinical trial participation, we respectfully call on the clinical trial community to respond to our call to action and consider our proposed action plan. Ideally, when patients want to search for clinical trials designed with patients for patients, we should be able to find them. A plain language summary for this publication is available in the supplementary material for this paper.
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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 | 0.372 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.040 | 0.042 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".