A Comparison of Patient Knowledge of Clinical Trials and Trialist Priorities
Bibliographic record
Abstract
BACKGROUND: Recruitment to clinical trials remains poor, and patient knowledge of clinical trials is one barrier to recruitment. To identify knowledge deficits, we conducted and compared surveys measuring actual patient knowledge and clinical trialist priorities for patient knowledge. METHODS: Consenting patients at a tertiary cancer centre answered a survey that included 2 opinion questions about their own knowledge and willingness to join a trial, and22 knowledge questions. Clinical researchers at the centre were asked 13 questions about the importance of various trials factors. RESULTS: Of 126 patients surveyed, 16% had joined a clinical trial, and 42% had a secondary school education or less. The mean correct response rate on the knowledge questions was 58%. Higher rates of correct responses were associated with lower age (p = 0.05), greater education (p = 0.006), prior trial participation (p < 0.001), agreement or strong agreement with perceived understanding of trials (p < 0.001), and willingness to join a clinical trial (p = 0.002). Trialists valued an understanding of the rationale for clinical trials and of randomization, placebo, and patient protection, but those particular topics were poorly understood by patients. CONCLUSIONS: Patient knowledge about clinical trials is poor, including knowledge of several concepts ranked important by clinical trialists. The findings suggest that when developing education interventions, emphasis should be placed on the topics most directly related to patient care, and factors such as age and education level should be considered.
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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 | 0.021 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".