3. Clinical Trials Knowledge in Oncology Patients: a Comparison of Actual Knowledge versus Trialists’ Priorities
Bibliographic record
Abstract
Background: Advancements in oncology depend on clinical trials, yet recruitment to trials remains poor. Previous efforts to increase enrolment by providing educational materialsto patients have improved patient understanding of trials, but not recruitment. To understand the clinical trials knowledge gaps among oncology patients, surveys of patients and trialists were conducted and compared. Methods: Patients completed a questionnaire measuring their understanding of key concepts in clinical trials. Twenty-two “true/false/do not know” knowledge questions, two 5-point Likert opinion questions, one free-text space and demographics were collected. Trialists (nurses and physicians) completed 13 five-point Likert scale questions plus freetext space to measure the importance they placed on patient knowledge of specific topics. The relationship between what trialists valued and actual patient knowledge was compared. Results: Patients thought they had a good understanding of clinical trials (50%) however this apparent understanding of clinical trials was not reflected in the scoring as only 58.3% (SD 23.5) of questions were answered correctly. There were positive associations shown between education level, personal belief of understanding and willingness to join a clinical trial with percentage of correct responses (p=0.006, p<0.001, p=0.002 respectively). For topics given high knowledge priority by trialists, patients gave correct answers for lessthan 50%. Conclusion: Among patients with cancer, there is a poor knowledge of clinical trials and a gap between what trialists think patients ought to know and actual patientunderstanding. These results support the development of educational materials on clinical trials for oncology.
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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 | 0.013 | 0.051 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".